No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
Challenges and Opportunities Facing Medical Education
Abstract
Medical education is at a crossroads. Although unique features exist at the undergraduate, graduate, and continuing education levels, shared aspects of all three levels are especially revealing, and form the basis for informed decision-making about the future of medical education.This paper describes some of the internal and external challenges confronting undergraduate medical education. Key internal challenges include the focus on disease to the relative exclusion of behavior, inpatient versus outpatient education, and implications of a faculty whose research is highly focused at the molecular or submolecular level. External factors include the exponential growth in knowledge, associated technologic ("disruptive") innovations, and societal changes. Addressing these challenges requires decisive institutional leadership with an eye to 2020 and beyond--the period in which current matriculants will begin their careers. This paper presents a spiral-model format for a curriculum of medical education, based on disease mechanisms, that addresses many of these challenges and incorporates sound educational principles.
... In 1950, the estimated doubling time of medical knowledge was 50 years. 1 By 2010, that figure had shrunk to 3.5 years, and in 2020 medical knowledge was cited to double every 73 days. 1 To accommodate for this explosive growth in literature, an increasing number of clinical guidelines are both produced and updated to provide physicians with evidence based recommendations for patient treatment. 2,3 Between 1990 and 2012, the number of clinical guidelines grew over a hundred-fold, from 73 in 1990 to over 7,500 in 2012. 2 This increase in the number of clinical guidelines has made it especially difficult for general internal medicine physicians to access nuanced up-to-date guideline recommendations, as many of them need to manage multisystem disease in patients with a variety of comorbidities. ...
... 1 By 2010, that figure had shrunk to 3.5 years, and in 2020 medical knowledge was cited to double every 73 days. 1 To accommodate for this explosive growth in literature, an increasing number of clinical guidelines are both produced and updated to provide physicians with evidence based recommendations for patient treatment. 2,3 Between 1990 and 2012, the number of clinical guidelines grew over a hundred-fold, from 73 in 1990 to over 7,500 in 2012. 2 This increase in the number of clinical guidelines has made it especially difficult for general internal medicine physicians to access nuanced up-to-date guideline recommendations, as many of them need to manage multisystem disease in patients with a variety of comorbidities. ...
... The rapid growth of medical knowledge has resulted in medical students learning only 6% of their overall clinical knowledge during training. 1 This study is especially timely given the ongoing trends of physician shortages, implementation of electronic triage systems, and increasing complexity of modern guidelines. [37][38][39][40][41][42][43] The three guidelines selected provide a blend of recommendations for conditions encountered by internal medicine physicians and are related to fields such as endocrinology, cardiology, and critical care. ...
Introduction:
Physicians treating multisystem diseases face challenges in consulting expanding, complex clinical guidelines. Large language models like ChatGPT may help consolidate this information, providing quick access to guideline recommendations. The objective of this study was to assess the accuracy of ChatGPT 3.5 and 4o responses to questions based on specialist-level guideline recommendations.
Methods:
A framework was developed for authors to pose questions, based on a guideline recommendation, to ChatGPT. A validation tool graded responses as concordant, partially concordant, or discordant to the guideline recommendation. A total of 581 recommendations from three guidelines were analyzed. The primary outcome was overall accuracy. Subgroup analyses assessed accuracy based on number of criteria, strength of evidence, and type of recommendation.
Results:
For ChatGPT 3.5, 347 recommendations were concordant (59.72%), 128 partially concordant (22.03%), and 106 discordant (18.24%). Questions seeking a single response (Z = 5.289, p < .001) and questions based on recommendations with strong levels of evidence (OR 2.23, p = .001) generated higher levels of concordance. For ChatGPT 4o, 474 recommendations were concordant (81.6%), 82 partially concordant (14.1%), and 25 discordant (4.3%). Mean concordance ratings for single questions were significantly higher compared to multipart questions (Z = 3.08, p = .002). Mean concordance ratings for ChatGPT 4o were substantially higher compared to ChatGPT 3.5 (Z = 8.66, p < .00001).
Discussion:
ChatGPT 3.5 had a moderate level of accuracy. There remain weaknesses in its ability to answer multi-part questions or those backed by weaker evidence. ChatGPT 4o performed substantially better than ChatGPT 3.5, though both models were vulnerable to hallucination.
... Without fostering a fundamental understanding of science during training, there is a risk of a shortage of future researchers in medical science [9], [20]. This problem has already been recognised, and for years, there have been calls from various directions to improve scientific education in medical studies, not only due to the rapid growth of knowledge and technological advancements [1], [3], [4], [5], [6], [23], [21], [25], [33]. The Association of the Scientific Medical Societies in Germany (2008) [25], the German Research Foundation (2010) [6], the German Council of Science and Humanities (2014) [33], and the Association of Medical Faculties (2016) [22] have long acknowledged the necessity for the enhanced acquisition of fundamental scientific competencies throughout the course of medical studies. ...
... Ohne die Vermittlung eines wissenschaftlichen Grundverständnisses in der Ausbildung droht ein Nachwuchsmangel in der medizinischen Forschung [9], [20]. Dieses Problem wurde bereits erkannt, und nicht nur aufgrund des rasanten Wissenszuwachses und technologischen Fortschritts wird seit Jahren von verschiedenen Seiten eine Verbesserung der wissenschaftlichen Ausbildung im Medizinstudium gefordert [1], [3], [4], [5], [6], [23], [21], [25], [33]. Die Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften e.V. (2008) [25], die Deutsche Forschungsgemeinschaft (2010) [6], der Wissenschaftsrat (2014) [33], sowie der Medizinische Fakultätentag (2016) [22] sehen seit Jahren die Notwendigkeit, dass grundlegende wissenschaftliche Kompetenzen im Medizinstudium verstärkt erworben werden sollten. ...
We propose a new approach to deriving minimum standards for scientific training in medical studies. This approach allows specific learning objectives to be clearly defined and presented, in an easily comprehensible manner. We contend that a fundamental prerequisite for university studies is the instruction in the systematic scientific method that can be described through the scientific cycle. This instruction provides the foundation for the acquisition of scientific knowledge and evidence-based practice in medicine.
... At the Uniformed Services University, medical residents are being exposed to AI technology to interpret X-rays and histopathology slides, not only making them familiar with the software, but teaching them its strengths and pitfalls [93]. Considering that the current amount of medical knowledge doubles every 75 days, leveraging AI will be crucial for physicians to maintain and improve the quality of care for service members [94]. ...
... Services University, medical residents are being exposed to AI technology to interpret Xrays and histopathology slides, not only making them familiar with the software, but teaching them its strengths and pitfalls [93]. Considering that the current amount of medical knowledge doubles every 75 days, leveraging AI will be crucial for physicians to maintain and improve the quality of care for service members [94]. ...
Conducted in challenging environments such as disaster or conflict areas, operational medicine presents unique challenges for the delivery of efficient and quality healthcare. It exposes first responders and medical personnel to many unexpected health risks and dangerous situations. To tackle these issues, artificial intelligence (AI) has been progressively incorporated into operational medicine, both on the front lines and also more recently in support roles. The ability of AI to rapidly analyze high-dimensional data and make inferences has opened up a wide variety of opportunities and increased efficiency for its early adopters, notably for the United States military, for non-invasive medical imaging and for mental health applications. This review discusses the current state of AI and highlights its broad array of potential applications in operational medicine as developed for the United States military.
... They need to be equipped with lifelong learning skills to meet the expectations. [1] Accreditation bodies including ACGME emphasize providing opportunities for students to develop lifelong learning skills. [1] Self-directed learning (SDL) is one, motivating students toward a continuous learning process with the acquisition of additional skills like critical thinking, team building, interpersonal communication, and becoming a self-regulated learner. ...
... [1] Accreditation bodies including ACGME emphasize providing opportunities for students to develop lifelong learning skills. [1] Self-directed learning (SDL) is one, motivating students toward a continuous learning process with the acquisition of additional skills like critical thinking, team building, interpersonal communication, and becoming a self-regulated learner. Thus, the National Medical Commission (NMC) of India has incorporated SDL as formal teaching for undergraduates as a mandatory component of the Competency-Based Medical Education (CBME) curriculum. ...
BACKGROUND
Healthcare professionals must be lifelong learners to deal with the challenge of technological advancements and scientific information load. Global accrediting organizations promote self-directed learning (SDL) for development of lifelong learning skills in medical graduates and are adopted in India with introduction of Competency-Based Medical Education. The aim of this study is to develop Faculty Self-Directed Learning Readiness and Perception Scale (FSDLRPS) suited for Indian context.
MATERIALS AND METHODS
This is an observational and analytical study designed to develop and implement a validated scale suited to study the faculty readiness and perception toward SDL in Indian healthcare institutions using the Delphi technique. A 23-item FSDLRPS for healthcare educators was developed using a 12-member expert panel. The scale was implemented on healthcare educators after pilot testing. Demographic data, perceptions, and readiness were studied. Descriptive statistics, analysis of variance, and Friedman ranking test were performed, and Cronbach’s alpha was calculated using Microsoft Excel 365 and SPSS Ver. 15. Open-ended questions were analyzed by thematic analysis.
RESULTS
The Content Validity Index and Cronbach’s alpha scores for the final Delphi round for readiness were 1.0 and 0.779, while those for the final Delphi round for the perception items were 0.935 and 0.900, respectively. It was implemented on 163 participants from 12 Indian states and revealed significant associations between faculty perceptions of SDL and a few variables. Readiness to implement was less in some areas like facilitation skills.
CONCLUSIONS
Faculty perceived SDL as feasible and a good opportunity to help students acquire multiple skills but are not ready due to lack of knowledge and facilitation skills. They seek continuous support to increase their level of readiness.
... In 2020, the doubling time of medical knowledge was projected to be just 73 days-far faster than our ability to assimilate new information. 1 The pace of updates and breadth of medical knowledge call for fundamental change in how educators prepare GME graduates for a future of lifelong self-directed learning (SDL). [1][2][3] As GME emphasizes SDL, coaching will be essential to guide residents in obtaining this skill by providing the needed scaffolding. ...
... 1 The pace of updates and breadth of medical knowledge call for fundamental change in how educators prepare GME graduates for a future of lifelong self-directed learning (SDL). [1][2][3] As GME emphasizes SDL, coaching will be essential to guide residents in obtaining this skill by providing the needed scaffolding. [3][4][5] Based on Vygotsky's concept of the zone of proximal development (ZPD), which describes the gap between what a learner can do independently and what they can achieve with guidance and support from a more knowledgeable individual, scaffolding involves structured support from an educator that helps trainees progress beyond their current abilities to achieve higher levels of understanding and independence. ...
Background
Graduate medical education is a critical period for fostering self-directed learning (SDL). This study introduced an academic coaching program to support SDL among internal medicine (IM) residents, leveraging Gallimore and Tharp’s four-stage model as a scaffolding framework.
Objective
To assess the impact of academic coaching on residents’ performance, including Internal Medicine In-Training Examination (IM-ITE) scores, individualized learning plans (ILPs), and attitudinal changes. The study also explored how coaching influenced SDL within the residency program.
Design and Participants
A mixed-methods case study was conducted in a mid-sized university’s IM residency program. Quantitative measures included pre- and post-coaching surveys, ILP analysis, and IM-ITE score evaluation. Semi-structured interviews provided qualitative insights into participant experiences. Of 77 eligible residents, 40 enrolled in the coaching program, and 27 (18 post-graduate year (PGY) 1 and PGY2) completed at least one session. Baseline IM-ITE scores guided enrollment for mandatory participants.
Key Results
Of the 77 residents, 51 had complete IM-ITE data and individualized learning plans (ILPs) from 2022 and 2023. Residents attending one coaching session demonstrated significant improvement in IM-ITE percentile scores ( p = .022), while those with two or more sessions showed significant gains in both percent correct ( p = .015) and percentile scores ( p = .003). No significant differences were observed in ILPs or attitudinal surveys. Qualitative analyses of resident participant interviews highlight coaching’s positive influence on SDL, organized into input, process, and output domains.
Conclusions
Sustained coaching, defined as two or more coaching meetings, is associated with improved IM-ITE performance. Qualitative findings underscore the program’s role in enhancing residents’ SDL.
... In 2020, it was projected to be just 73 days. 1 By the time, a medical school student completes the minimum length of training (7 years) needed to practice medicine, they would have experienced approximately three doublings in knowledge. 1 This expansion of knowledge has forced physicians to consistently redefine concepts related to clinical care pathways. ...
... In 2020, it was projected to be just 73 days. 1 By the time, a medical school student completes the minimum length of training (7 years) needed to practice medicine, they would have experienced approximately three doublings in knowledge. 1 This expansion of knowledge has forced physicians to consistently redefine concepts related to clinical care pathways. ...
Background
We undertook this study to evaluate the efficacy of an on-table extubation protocol and to assess the magnitude of benefits when implemented as a routine practice in a developing country.
Methods
This prospective observational study at a single tertiary care referral hospital was designed to determine the efficacy of an on-table extubation protocol when applied to children undergoing cardiac surgery in the developing world. The study included 226 patients who were 1 month to 18 years of age undergoing cardiac surgery (including grown-up congenital heart disease [GUCHD] patients). Patients with RACHS score ≥ 4, neonates, preoperatively ventilated children, and emergency surgeries were excluded from the study. All pediatric elective cardiac surgical patients belonging to RACHS 1, 2, and 3 categories were considered as potential candidates for on-table extubation. Trial registration: Clinical Trials Registry of India (CTRI/2020/07/026567).
Results
Among the 226 children who underwent elective cardiac surgeries, we were able to extubate 142 patients (62.83%) in the operating room. This included 46.6% (54/116) infants, 80.8% (38/47) children less than 5 years of age, 79.3% (46/58) children between 5 years to 18 years age, and 80% (4/5) GUCHD. The duration of intensive care unit (ICU) stay, hospital stay, and hospital cost were significantly less in the on-table extubation group (23 [20, 26] hours; 102 [97, 125] hours; INR 2,09,011 [181032, 244298]) as compared with those patients extubated in the ICU within 6 hours (28 [22, 46] hours; 122 [100, 168] hours; INR 2,25,430 [162203, 273831]) and beyond 6 hours (71 [45, 121] hours; 184 [127, 243] hours; INR 2,53,541 [226838, 306871]).
Conclusions
This protocol shows a significant reduction in ICU stay, hospital stay, and total hospital cost when compared with either extubation within 6 h in the ICU or delayed extubation (beyond 6 h) in patients undergoing pediatric cardiac surgery.
... They are inherently multidisciplinary, involving collaboration among various professions from various departments (Ruiz et al. 2012). Furthermore, healthcare processes are constantly adapting to the evolving and advancing medical knowledge, which necessitates rapid development of new medications and treatments (Ruiz et al. 2012;Densen 2011). Moreover, healthcare processes are significantly influenced by unpredictable issues, context-related information, such as the state of a system or patient characteristics (Munoz-Gama et al. 2022). ...
Many doctors and nurses in hospitals come into contact with problems related to healthcare processes on a daily basis. Although the research communities have already delved deep into healthcare processes, it is not always evident whether the research is prioritizing the most pertinent topics or adequately addressing the most pressing issues faced by healthcare professionals. It is essential to bridge the gap between research and practice, ensuring that studies align with the real-world challenges encountered by nurses and doctors in their everyday work. This Delphi study researched what process-related problems exist from the perspective of physicians and nurses in a hospital, and what is their comparative relevance. In total, 18 doctors and 7 nurses participated in this study. The Delphi study consisted of six rounds and identified a list of 44 process-related problems over seven different categories. The obtained list can help both hospitals and researchers working on solutions for relevant problems.
... As of 2020, the doubling time of new medical information available to learners was estimated at just 73 days, compared to 3.5 years in 2010 and 7 years in 1980. 2 Current areas of AI use in medical education include developing curriculum, providing feedback to learners, and delivering content. 3,4 Chatbots are commonly used AI tools that use natural language processing models to interpret queries made by users and produce a response synthesizing large amounts of information from the Internet. ...
Background and Objectives: Primary care physicians are well-positioned to be at the forefront of screening for and treating substance use disorders (SUDs). In addition, the Accreditation Council for Graduate Medical Education has deemed addiction training a common program requirement for all residency programs. With less than one-third of family medicine residency programs providing addiction training, understanding best practices for addiction training is important. Methods: We interviewed 12 faculty at family medicine residency programs across the country who have a strong reputation for addiction training. We analyzed interview transcripts thematically to identify best practices for creating and providing addiction curricula. Results: Creating an addiction curriculum originates with an addiction champion who garners the support of clinical leadership and provides faculty development that is augmented by a multidisciplinary team of providers, often grant-supported. Coupling didactic learning with a wide array of experiential opportunities is important, particularly allowing residents to care for patients with SUDs longitudinally in their primary care clinics. Residency programs should anticipate stigma and associated resistance from clinic staff and providers and should work collaboratively to mitigate these. Conclusions: Comprehensive and robust addiction training in family medicine residency training should include didactic and experiential learning opportunities with a well-supported and philosophically aligned clinical and educational culture that values caring for patients with SUDs.
... 27 Recently, the rapid increase in the amount of available medical information has become a challenge in medical education. 28 Therefore, it is necessary to identify credible information from numerous resources. In their experience with the COVID-19 pandemic, Lungeanu et al noted that knowledge of data science and the ability to understand the results of research papers can be critical complements to clinical practice. ...
Background
The coronavirus disease 2019 (COVID-19) pandemic has led to considerable advances in medical education through technological integration. The crisis generated by the pandemic in medical practice, education, and evolving technology has led to changes in the skills of medical professionals. This study aimed to examine the competencies required of medical students in the post-pandemic era.
Methods
We conducted 2 mixed-methods studies. Study 1 explored medical students’ necessary competencies after the COVID-19 pandemic. We conducted group work with faculty members and students from the Chiba University School of Medicine, captured proposed competencies, discussed them, and qualitatively analyzed the group work data using content analysis to extract the competencies. Study 2 was a secondary data analysis that compared the categories identified in Study 1 with the competencies required prior to the COVID-19 pandemic, which were extracted from the websites of all 82 medical schools and colleges in Japan, to identify the differences in competencies before and after the pandemic.
Results
Study 1 resulted in the identification of 12 categories and 62 subcategories. The results of Study 2 showed that the increased occurrence of competencies was related to the utilization of information and communication technology (ICT) and artificial intelligence (AI), self-management, information gathering and explanation, liberal arts and generic skills, and exploring medicine and medical care/research presentations. The prevalence rates of these factors prior to the COVID-19 outbreak were 17.1%, 28.0%, 39.0%, 41.5%, and 48.8%, respectively.
Conclusions
Competency-based medical education in ICT, self-management, and medical exploration has become increasingly important after the pandemic. Therefore, it is necessary to develop an educational curriculum to enable medical students to acquire these competencies. The study findings contribute to the literature on medical education and offer valuable insight into setting effective academic goals and designing suitable curricula for undergraduate medical students in the post-pandemic era.
... This expansion of knowledge will force medical schools and residency programmes to redefine the essential core of what students must learn and how they have to do it. 33 This integration of AI in different medical fields should also be associated with creating guidelines regarding ethical aspects, privacy, data security and patient autonomy. Professionals such as clinicians, computer scientists, and ethicists must develop AI tools that prove ethically safe and scientifically accurate. ...
Objectives
The aim of this study was to compare the performances of residents and ChatGPT in answering validated questions and assess paediatric surgery residents’ acceptance, perceptions and readiness to integrate artificial intelligence (AI) into clinical practice.
Methods
We conducted a cross-sectional study using randomly selected questions and clinical cases on paediatric surgery topics. We examined residents’ acceptance of AI before and after comparing their results to ChatGPT’s results using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. Data analysis was performed using Jamovi V.2.4.12.0.
Results
30 residents participated. ChatGPT-4.0’s median score was 13.75, while ChatGPT-3.5’s was 8.75. The median score among residents was 8.13. Differences appeared statistically significant. ChatGPT outperformed residents specifically in definition questions (ChatGPT-4.0 vs residents, p<0.0001; ChatGPT-3.5 vs residents, p=0.03). In the UTAUT2 Questionnaire, respondents expressed a more positive evaluation of ChatGPT with higher mean values for each construct and lower fear of technology after learning about test scores.
Discussion
ChatGPT performed better than residents in knowledge-based questions and simple clinical cases. The accuracy of ChatGPT declined when confronted with more complex questions. The UTAUT questionnaire results showed that learning about the potential of ChatGPT could lead to a shift in perception, resulting in a more positive attitude towards AI.
Conclusion
Our study reveals residents’ positive receptivity towards AI, especially after being confronted with its efficacy. These results highlight the importance of integrating AI-related topics into medical curricula and residency to help future physicians and surgeons better understand the advantages and limitations of AI.
... First, existing LLMs in Modern Medicine fail to capture the dynamic of updated medical knowledge, while factual knowledge in medicine is subject to change over time. The research found that the doubling time of medical knowledge in 1950 was 50 years, and in 2010, 3.5 years, and it is projected to be just 73 days in 2020 [70] (The solution will be discussed in Section 5.1.1). ...
Integrating Traditional Chinese Medicine (TCM) and Modern Medicine faces significant barriers, including the absence of unified frameworks and standardized diagnostic criteria. While Large Language Models (LLMs) in Medicine hold transformative potential to bridge these gaps, their application in integrative medicine remains underexplored and methodologically fragmented. This review systematically examines LLMs' development, deployment, and challenges in harmonizing Modern and TCM practices while identifying actionable strategies to advance this emerging field. This review aimed to provide insight into the following aspects. First, it summarized the existing LLMs in the General Domain, Modern Medicine, and TCM from the perspective of their model structures, number of parameters and domain‐specific training data. We highlighted the limitations of existing LLMs in integrative medicine tasks through benchmark experiments and the unique applications of LLMs in Integrative Medicine. We discussed the challenges during the development and proposed possible solutions to mitigate them. This review synthesizes technical insights with practical clinical considerations, providing a roadmap for leveraging LLMs to bridge TCM's empirical wisdom with modern medical systems. These AI‐driven synergies could redefine personalized care, optimize therapeutic outcomes, and establish new standards for holistic healthcare innovation.
... This rapid growth of textual information highlights the critical need for effective methods to classify, organize, and retrieve relevant information efficiently. This challenge is amplified by the doubling of medical knowledge every 73 days, as estimated in 2020, compared to every 7 years in 2010[2]. Consequently, there is a growing demand for robust, automated solutions that can streamline the classification and retrieval of textual data, ensuring timely and accurate decision-making. ...
BACKGROUND
The exponential growth of digital information has led to an unprecedented expansion in the volume of unstructured text data. Efficient classification of these articles is critical for timely evidence synthesis and informed decision-making in healthcare. Machine learning techniques have shown considerable promise for text classification tasks. However, multiclass classification of articles by study publication type has been largely overlooked compared to binary or multilabel classification. Addressing this gap could significantlyThe objective of this study was to fine-tune and evaluate domain-specific transformer-based language models on a gold-standard dataset for multiclass classification of clinical literature into mutually exclusive categories: original study, review, evidence-based guideline, and non-experimental. enhance knowledge translation workflows and support systematic review processes.
OBJECTIVE
The objective of this study was to fine-tune and evaluate domain-specific transformer-based language models on a gold-standard dataset for multiclass classification of clinical literature into mutually exclusive categories: original study, review, evidence-based guideline, and non-experimental.
METHODS
The titles and abstracts of McMaster’s Premium LiteratUre Service (PLUS) dataset of 162,380 articles were used for fine-tuning 7 domain-specific transformers. Clinical experts classified articles into four mutually exclusive publication types. PLUS data were split 80:10:10 for training, validation, and testing, with Clinical Hedges used for external validation. A grid search evaluated the impact of class weight adjustments, learning rate, batch size, warmup ratio, and weight decay, totaling 1,890 configurations. Models were assessed using 10 metrics, including area under the receiver operating characteristic curve (AUROC), F1 score, and Matthew’s correlation coefficient (MCC). Performance of individual classes was assessed using a one-to-rest approach, and overall performance was assessed using macro average. Optimal models identified from validation results were further tested on both PLUS and Clinical Hedges datasets, with calibration assessed visually.
RESULTS
Ten best-performing models achieved macro AUROC ≥0.99, F1 ≥0.89 and MCC ≥0.88 on the validation and test sets. Performance declined on Clinical Hedges. Models were consistently better at classifying original studies and reviews. BioBERT-based models had superior calibration performance, especially for original studies and reviews. Optimal configurations for search included lower learning rates (1E-5 and 3E-5), mid-range batch sizes (32–128), and lower weight decay (0.005-0.010). Class weight adjustments improved recall but generally reduced performance in other metrics. Models generally struggled with accurately classifying non-experimental and guideline articles, potentially due to class imbalance and content heterogeneity.
CONCLUSIONS
This study utilized a comprehensive hyperparameter search to highlight the effectiveness of fine-tuned transformer models, notably BioBERT variants, for multiclass clinical literature classification. While class weighting generally decreased overall performance, addressing class imbalance through alternative methods such as hierarchical classification or targeted resampling warrants future exploration. Optimal hyperparameter configurations were crucial for robust performance, aligning with previous literature. These findings support future modelling research and the practical deployment in human-in-the-loop systems to support knowledge synthesis and translation workflows using optimal configurations found in this work.
... Healthcare delivery is increasingly strained by the expansion of biomedical knowledge, driven by advancements in research technologies, molecular investigations, drug development, and clinical trials [1][2][3] . This proliferation challenges healthcare systems and practitioners, as clinicians face difficulty in staying current, particularly as their formal training may become outdated. ...
Large Language Models (LLMs) have great potential in the field of health care, yet they face great challenges in adapting to rapidly evolving medical knowledge. This can lead to outdated or contradictory treatment suggestions. This study investigated how LLMs respond to evolving clinical guidelines, focusing on concept drift and internal inconsistencies. We developed the DriftMedQA benchmark to simulate guideline evolution and assessed the temporal reliability of various LLMs. Our evaluation of seven state-of-the-art models across 4,290 scenarios demonstrated difficulties in rejecting outdated recommendations and frequently endorsing conflicting guidance. Additionally, we explored two mitigation strategies: Retrieval-Augmented Generation and preference fine-tuning via Direct Preference Optimization. While each method improved model performance, their combination led to the most consistent and reliable results. These findings underscore the need to improve LLM robustness to temporal shifts to ensure more dependable applications in clinical practice.
... However, in terms of professional practice ability, the performance of ChatGPT-4 and Claude 3 is less sat-isfactory, with accuracy rates of only 53.3% and 45.6% respectively. The reasons for this may include the insufficient depth in the learning of clinical practice by artificial intelligence and outdated updates on clinical guidelines or procedural standards, making it difficult to integrate knowledge points thoroughly, leading to incomplete case analyses [18][19][20][21]. Other reasons can be that the test items for professional practice ability involve multiple-choice questions with one or more correct answers, and the answers may have dependencies or sequences, which could make it difficult for artificial intelligence to discern the correct information. ...
Aim: To evaluate the effectiveness of two large language models, ChatGPT-4 and Claude 3, in improving the accuracy of question responses by senior sonologist and junior sonologist.Material and methods: A senior and a junior sonologist were given a practice exam. After answering the questions, they reviewed the responses and explanations provided by ChatGPT-4 and Claude 3. The accuracy and scores before and after incorporating the models' input were analyzed to compare their effectiveness.Results: No statistically significant differences were found between the two models' responses scores for each section (all p>0.05). For junior sonologist, both ChatGPT-4 (p=0.039) and Claude 3 (p=0.039) significantly improved scores in basic knowledge. The responses provided by ChatGPT-4 also significantly improved scores in relevant professional knowledge (p=0.038), though their explanations did not (p=0.077). For all exam sections, both models' responses and explanations significantly improved scores (all p<0.05). For senior sonologist, both ChatGPT-4's responses (p=0.022) and explanations (p=0.034) improved scores in basic knowledge, as did Claude 3's explanations (p=0.003). Across all sections, Claude 3's explanations significantly improved scores (p=0.041).Conclusion: ChatGPT-4 and Claude 3 significantly improved sonologist' examination performance, particularly in basic knowledge.
... 8 Modern additions such as bioethics and sociology are competing with the traditional disciplines of anatomy and physiology; mandates and pressures to continuously add new content are causing the already packed curricula of medical schools to outgrow their long-established Flexnerian structures. 9 Knowledge is both expanding exponentially 10 with an ever-reducing doubling time, and new facts are almost as rapidly then becoming outdated. This halflife effect of medical knowledge further challenges medical education in the selection of suitable material for curricula. ...
Objectives
Several stakeholders are formally recognised when designing undergraduate medical curricula, but past studies have failed to identify them with sufficient breadth, to explore their understanding of the system, or examine their views on curriculum composition. This qualitative study drew on elements of systems thinking to better understand the stakeholders in undergraduate medical education and their role and priorities in curriculum composition.
Methods
This study employed an exploratory qualitative methodology. Participants were initially identified from the General Medical Council's list of stakeholders and were recruited using a combination of convenience, judgmental and snowball sampling. Data were collected through semistructured interviewing. Interviews were descriptively coded and then thematically analysed.
Results
In total, 18 participants were interviewed about their perspectives on stakeholders, the purpose of the education, along with their ideal weightings for curriculum subjects. The findings suggested that the breadth of stakeholders exceeded the modest list provided by the General Medical Council. The purposes of the education were themed into: (1) safe patient care, (2) social benefit, (3) service provision, (4) student benefit and (5) provider benefit. Safe patient care emerged as a universally shared purpose, although views on the customer varied between participants. Curricula priorities were more diverse, with competing interests favouring different subjects for emphasis in the curriculum, with views on the value of scientific-learning particularly divided.
Conclusion
Undergraduate medical education likely concerns a broader range of stakeholders than are often engaged. Several stakeholders are formally recognised when designing undergraduate medical curricula but past studies have failed to identify them with sufficient breadth, to explore their understanding of the system, or to examine their views on curriculum composition. This research raised questions about engagement of vital stakeholders and how power is distributed in the system, along with the need to develop roles into the future when renewing curricula.
... 21 Medical knowledge continues to advance rapidly, and devoting more curriculum time to specific diseases is unlikely to be an effective strategy. 22 If expanding time and resources dedicated to MASH education is not an option, then programs should consider where MASH resides in the larger framework of diseases covered in graduate medical education. ...
Purpose
Metabolic dysfunction–associated steatotic liver disease (MASLD) and its inflammatory subtype, metabolic dysfunction–associated steatohepatitis (MASH), are associated with cardiometabolic risk factors, including obesity and type 2 diabetes. The prevalence of both conditions is rising rapidly and is underdiagnosed (<5%). We aimed to gather qualitative and quantitative insights from program leaders in US medical education training on their experience with MASH-related training and education.
Participants and methods
A cross-sectional study consisting of a quantitative survey and qualitative discussions with individuals in primary care (internal medicine and family medicine) and specialty programs (hepatology, gastroenterology, and endocrinology) were held from February 21 to August 28, 2023. Descriptive statistics were used for data analysis.
Results
A total of 190 leaders participated in the online survey and 11 leaders joined the focus groups. Almost all respondents reported that MASLD (96%) and MASH (92%) were included in their program’s curricula. However, many believed that little time was devoted to discussing MASH in their program. Most respondents agreed that MASH is extremely underdiagnosed. Program leaders agreed that the interconnectedness of MASH with other cardiometabolic conditions necessitates instruction time on MASH beyond that of its dedicated curriculum time. All participants believed that emergence of regulatory-approved drugs for MASH will drive a decision to increase the time allotted for MASH in the curriculum.
Conclusion
Although program leaders agreed that MASH has an important place in medical education curricula, the relative paucity of treatment options reduces its coverage in training, thereby limiting healthcare practitioners’ understanding of MASH.
... It was projected that technical knowledge would double in 50 years, 7 years, and 3. 5 years, respectively, in 1950, 1980, and 2010. It is predicted to be just 73 days in 2020 (Densen, 2011). In addition to time and cognitive limitations, the sheer amount of information exceeded the ability of tech professionals to use this new information (Thillaivasan & Wickramasinghe, 2020). ...
In order to solve the workplace identified problems, this research major objective is to ascertain the nature and actual impact of Artificial Intelligence (AI) on employee’s productivity. This study
adopted a descriptive survey research method through the use of primary data source instruments of questionnaires shared to over 150 employees of technological firms in Delta state. This study
found that Artificial intelligence (AI) has the potential to revolutionize the workplace by greatly increasing worker engagement and productivity. The study empirically proved that Artificial Intelligence impacts positively and significantly on the sustainability of employee engagement. Secondly, the study empirically revealed that there is a positive and significant connection between Artificial Intelligence and employee’s attitude to work in organizations. This means that the AI introduction has made employees to develop a positive work attitude in order to keep their jobs from being taking over by automation powered AI machines. The study analyses proved that
Artificial Intelligence (AI) has a positive and significant effect on employee’s productivity. AI may assist organizations in more efficiently accomplishing their objectives by automating
repetitive processes, customizing employee experiences, and encouraging improved communication and teamwork. To guarantee that AI's advantages are fully realized, it is crucial to
solve the difficulties and moral dilemmas related to its application. It was observed that Artificial Intelligence integration to workplace must be human-centric before it can lead to more employee engagement and increase in productivity. The study concludes that the welfare of employees should always come first when using AI, making ethical issues very important. The study recommends that Companies should aim for a human-centric strategy, making sure AI complements human strengths rather than takes their place in workspace.
... It was projected that technical knowledge would double in 50 years, 7 years, and 3. 5 years, respectively, in 1950, 1980, and 2010. It is predicted to be just 73 days in 2020 (Densen, 2011). In addition to time and cognitive limitations, the sheer amount of information exceeded the ability of tech professionals to use this new information (Thillaivasan & Wickramasinghe, 2020). ...
... It was projected that technical knowledge would double in 50 years, 7 years, and 3. 5 years, respectively, in 1950, 1980, and 2010. It is predicted to be just 73 days in 2020 (Densen, 2011). In addition to time and cognitive limitations, the sheer amount of information exceeded the ability of tech professionals to use this new information (Thillaivasan & Wickramasinghe, 2020). ...
... To create multiple-choice questions (MCQs) aimed at measuring higher-level skills like clinical reasoning instead of rote memorization, the most up-to-date medical information should be considered by integrating it with clinical practice. However, the half-life of medical knowledge is continuously and rapidly increasing (Densen 2011). Therefore, it may be difficult for medical educators to keep up with the latest developments in question production. ...
Objective: This study aims to evaluate the suitability of the automatic item generation (AIG) for producing Turkish case-based multiple-choice questions (MCQs) in psychiatry.
Method: The study was planned as a descriptive study. In the first stage, topics were determined and a cognitive model was created by subject matter experts. In the second stage, a question template was created, variables were determined, the format of answer options was organized, and two equivalent templates of question content with different combinations were created. In the final stage, questions were generated using Python-based software based on these models. Following the question generation, random samples were selected and evaluated by experienced educators using a structured form.
Results: A total of 1189 questions were generated, with 11 questions sampled for each diagnosis. In the evaluation conducted by experts, six of the questions were deemed appropriate for each parameter, while minor corrections were suggested for five questions. It was stated that all the questions assess clinical reasoning skills rather than factual recall. Conclusion: The template-based AIG method allows for the rapid and effective production of high-quality questions needed in medical education. The study demonstrated that AIG in the Turkish language for generating MCQs that assess clinical reasoning is applicable in the field of psychiatry. This method enables the production of a large number of questions in a short time, enriched with various combinations.
Keyword: Automated Item Generation, Clinical Reasoning, Medical Education, Multiple Choice Question, Psychiatry Education
... Clinical curriculum while preparing students for patient care frequently focuses on abnormal pathologies with normal development or physiology often being neglected or underemphasised (Densen, 2011). Developmental and behavioral paediatrics are integral components of pediatric clinical practice. ...
Introduction: Paediatric milestones provide a structured method for observing and monitoring a child’s progress and should be part of core paediatric curriculum. However, a literature review reveals that primary care physicians and pediatricians feel inadequate about their knowledge and practice of developmental paediatrics, thus exposing the lacunae in training. Methods: An intervention was planned amongst final-year medical undergraduate students in Oman during their paediatric rotation. A 90-minute multimodal active learning module incorporating diverse learning orientations was planned and administered as a skill-lab session. Its effectiveness in learner motivation, engagement, and faculty participation was evaluated using a questionnaire based on the ICAP (Interactive, Constructive, Active, and Passive) framework, administered to students at the end of the session. Results: Responses of the 62 participants indicated a significant association between their overall experience and tasks related to the active, constructive, and interactive elements of the module (p=0.001). The faculty’s role in facilitating the session significantly contributed to students’ overall experience (p=0.000). On linear regression, active, constructive, and interactive components of the module were moderate to high predictors of the participants’ overall learning experience. Conclusion: It was beneficial to base the teaching module on established learning theories. Active learning strategies proactively fostered student engagement and self-directed learning during the session. Faculty played an important role in planning and customising the content, flow, and delivery to maximise meaningful learning. Such interactive collaboration, especially for theoretical concepts in medicine, enables better student engagement, providing enhanced opportunities for learning, practice, and feedback.
... Az elméleti háttértudás a klinikai tanulmányokon túl az orvosi munka során is kiemelt jelentőségű, a klinikai gondolkodás és a döntéshozatal hatékonyságát növelheti [7]. Mind az elméleti, mind a klinikai tudományokra jellemző az exponenciálisan növekvő tudásanyag, becslések szerint az orvosi egyetem elvégzéséhez szükséges évek alatt az orvostudománnyal kapcsolatos ismereteink akár tízszeresre növekedhetnek [8]. Az elméleti tudományokhoz kapcsolódó ismeretek puszta mennyisége és összetettsége kihívást jelenthet az orvostanhallgatók számára nemcsak a vizsgák teljesítése, hanem a hosszú távú tudásmegőrzés szempontjából is. ...
Az orvosképzés hagyományosan elméleti és klinikai tanulmányokra válik szét, azonban egyre több orvosképző helyen jelenik meg a kettőt egymáshoz közelítő integrált kurrikulum. Az elméleti tudás stabil megszerzése kiemelten fontos, mivel ez alapozza meg a klinikai tantárgyakat, illetve a diplomaszerzés után a mindennapi klinikai gondolkodást és döntéshozatalt. Kutatások igazolják, hogy az elméleti tantárgyak vonatkozásában az egyetemi vizsgák után a hallgatók tárgyi tudása jelentősen csökkenhet, ami nehézségekhez vezethet a klinikai tanulmányok teljesítésében és akár az orvosi munkában. Korábbi tanulmányok szerint a hallgatók megközelítőleg a tanultak harmadát vagy negyedét felejtik el egy év leforgása alatt. Az egyes kutatásokban a felejtés mértéke ugyanakkor széles skálán mozog, tudományterületenként különböző. Az aktív tanulást támogató oktatási módszerek és tanulási stratégiák – mint az előhívási gyakorlatok és az időközönkénti ismétlés – bizonyítottan hatékonyan segítik elő a hosszú távú tudásmegőrzést. Az elméleti anyag klinikai kapcsolódási pontjainak hangsúlyozása, az integrált kurrikulum irányába tett lépések szintén hozzájárulhatnak a mélyebb megértéshez és a hosszú távú tudásmegőrzéshez. A jelen tanulmány célja átfogó jelleggel bemutatni az orvostanhallgatók hosszú távú tárgyi tudásával kapcsolatos tudományos eredményeket, valamint betekintést nyújtani az orvostanhallgatók körében is bizonyítottan hatékony tanulási-oktatási stratégiák területébe, amelyek alkalmazása hozzájárulhat az orvosképzés fejlődéséhez. Orv Hetil. 2025; 166(12): 450–458.
... Medical education presents students with complex and abstract topics, requiring them to understand, retain, and apply vast amounts of information in real time [1,2]. As students progress, their knowledge base expands, which can lead to challenges such as reduced retention, limited peer engagement, and increased burnout [3]. ...
Medical education faces the dual challenge of addressing cognitive overload and sustaining student engagement, particularly in complex subjects such as pharmacology. This study introduces Cinematic Clinical Narratives (CCNs) as an innovative approach to teaching antiparasitic pharmacology, combining generative artificial intelligence (genAI), edutainment, and mnemonic-based learning. The intervention involved two short films, Alien: Parasites Within and Wormquest, designed to teach antiparasitic pharmacology to first-year medical students. A control group of students only received traditional text-based clinical cases, while the experimental group engaged with the CCNs in an active learning environment. Students who received the CCN material scored an average of 8% higher on exam questions related to the material covered by the CCN compared to students in the control group. Results also showed that the CCNs improved engagement and interest among students, as evidenced by significantly higher scores on the Situational Interest Survey for Multimedia (SIS-M) compared to traditional methods. Notably, students preferred CCNs for their storytelling, visuals, and interactive elements. This study underscores the potential of CCNs as a supplementary educational tool, and suggests the potential for broader applications across other medical disciplines outside of antiparasitic pharmacology. By leveraging genAI and edutainment, CCNs represent a scalable and innovative approach to enhancing the medical learning experience.
... The evaluation of accessibility and availability of data is composed of timeliness and the amount of data available. From a medical perspective and considering the short development cycles for new medications and treatments [22], patients within the last 5 years are considered relevant for decision support. Annually, the Skin Tumor Center at the UKM records round about 220 new cases of patients with malignant melanoma. ...
This research focuses on evaluating and enhancing data readiness for the development of an Artificial Intelligence (AI)-based Clinical Decision Support System (CDSS) in the context of skin cancer treatment. The study, conducted at the Skin Tumor Center of the University Hospital M\"unster, delves into the essential role of data quality, availability, and extractability in implementing effective AI applications in oncology. By employing a multifaceted methodology, including literature review, data readiness assessment, and expert workshops, the study addresses the challenges of integrating AI into clinical decision-making. The research identifies crucial data points for skin cancer treatment decisions, evaluates their presence and quality in various information systems, and highlights the difficulties in extracting information from unstructured data. The findings underline the significance of high-quality, accessible data for the success of AI-driven CDSS in medical settings, particularly in the complex field of oncology.
... 30,31 Much of what is learned in medical school and training will change during practice. 32 Examples include (1) cardiac stenting may be no better than medical management for stable coronary artery disease, 33 (2) use of low-dose aspirin to prevent cardiac arrest may be harmful for many patients, 34 (3) many older adults are prescribed medications on the Beers list that may harm their health, 35 (4) many cancer treatments do not serve patient interests, 36,37 and (5) opioids are far riskier than commonly believed 20 years ago. 38,39 Although the restatement from the ALI will allow physicians to point to "prevailing professional practices," 29 it will also allow injured plaintiffs to point to the best scientific evidence and argue that a reasonable physician would have practiced accordingly. ...
Importance
Patients in the US have persistent needs for safe, evidence-based care. Physicians in the US report fear of liability risk and the need to practice “defensive medicine.” In 2024, the American Law Institute revised the legal standard for assessing medical negligence. Understanding the implications of this change is crucial for balancing patient safety, physician autonomy, and the legal system’s role in health care.
Observations
The updated standard from the American Law Institute shifts away from the traditional reliance on customary practice toward a more patient-centered concept of reasonable medical care. Although this revised standard still includes elements of prevailing medical practice, it defines reasonable care as the skill and knowledge regarded as competent among similar medical clinicians under comparable circumstances and acknowledges that, in some cases, juries can override customary practices if they fall short of contemporary standards. The restatement also embraces evidence-based practice guidelines, while leaving questions open about the variations in the quality of those guidelines. The restatement makes additional recommendations regarding informed consent and other aspects of physician-patient communication.
Conclusions and Relevance
The new standard of care from the American Law Institute represents a shift away from strict reliance on medical custom and invites courts to incorporate evidence-based medicine into malpractice law. Although states may adopt the recommendations from the American Law Institute at different times and to varying degrees, the restatement offers health professionals and the organizations in which they practice an opportunity to reconsider how medical negligence will be assessed, and to focus more directly on promoting patient safety and improving care delivery. Nonetheless, physicians should recognize that, at least for now, many courts will continue to rely significantly on prevailing practice in assessing medical liability.
... Residents, by virtue of their recent passage through the stages of medical education, possess a unique insight into the learning needs and challenges faced by their juniors. This positions them as pivotal figures in the educational ecosystem, capable of delivering tailored, contextually relevant instruction that resonates with the experiential learning pathways of medical students and interns [4] . ...
... However, significant obstacles must be addressed to implement personalised medicine approaches, including combining patient data from different sources and the need for extensive research to utilise this data effectively [65]. Unfortunately, the exponential growth of medical knowledge [35] outpaces our ability to effectively and efficiently integrate new information into clinical practice and research [115]. As a result, the medical field often struggles to iterate efficiently, leading to the slow adoption of novel techniques, treatments, and technologies [41,106], further complicating the development of personalised care approaches. ...
Abstract
Digital twin (DT) systems, which involve creating virtual replicas of physical objects or systems, have the potential to transform healthcare by offering personalised and predictive models that grant deeper insight into a patient's condition. This review explores current concepts in DT systems for musculoskeletal (MSK) applications through an overview of the key components, technologies, clinical uses, challenges, and future directions that define this rapidly growing field. DT systems leverage computational models such as multibody dynamics and finite element analysis to simulate the mechanical behaviour of MSK structures, while integration with wearable technologies allows real-time monitoring and feedback, facilitating preventive measures, and adaptive care strategies. Early applications of DT systems to MSK include optimising the monitoring of exercise and rehabilitation, analysing joint mechanics for personalised surgical techniques, and predicting post-operative outcomes. While still under development, these advancements promise to revolutionise MSK care by improving surgical planning, reducing complications, and personalising patient rehabilitation strategies. Integrating advanced machine learning algorithms can enhance the predictive abilities of DTs and provide a better understanding of disease processes through explainable artificial intelligence (AI). Despite their potential, DT systems face significant challenges. These include integrating multi-modal data, modelling ageing and damage, efficiently using computational resources and developing clinically accurate and impactful models. Addressing these challenges will require multidisciplinary collaboration. Furthermore, guaranteeing patient privacy and protection against bias is extremely important, as is navigating regulatory requirements for clinical adoption. DT systems present a significant opportunity to improve patient care, made possible by recent technological advancements in several fields, including wearable sensors, computational modelling of biological structures, and AI. As these technologies continue to mature and their integration is streamlined, DT systems may fast-track medical innovation, ushering in a new era of rapid improvement of treatment outcomes and broadening the scope of preventive medicine. Level of Evidence: Level V.
Keywords: artificial intelligence; digital twin; musculoskeletal; orthopaedic surgery; personalised medicine; rehabilitation.
... Learners acquired information via a combination of large group didactic lectures, small group learning experiences (e.g., anatomy lab with partners who shared a preserved human cadaver), and by relying on print textbooks and articles in journals that were available in institutional libraries or via inter-library loan. As the 21st century began, the rate of doubling of medical knowledge increased at an alarming pace, from 50 years in 1950 to about 73 days in 2021 [2,3]. It is no longer possible for a human brain to retain nor replace information at this rate, transforming today's clinician from a repository into a curator of knowledge. ...
Background
Institutions, departments, and individuals are increasingly facing challenges to determine how to enable their learners to acquire and curate rapidly changing knowledge and to foster the creation of lifelong learners in this information-rich digital era.
Methods
Much like the Precision Medicine initiative of 2015, in which diagnostic, treatment, and preventive care target individual patients based on their genetic and environmental profiles, educators can use the same principles to create a model of “Precision Education.”
Results
In this model, future facing individualizable educational infrastructure can consider innate qualities, learning style, behavior, environment, prior experience, expertise, and assessments.
Conclusion
Educators can utilize Artificial Intelligence, the Master Adaptive Learner model, and key components of Competency Based Medical Education to transform the evolution of Health Professions Education to meet the individual and systemic needs of tomorrow’s learners, educators, and institutions to improve educational and clinical outcomes.
... Self-directed learning has been shown to outperform traditionally taught groups in terms of performance [33] and lead to better clinical skills, knowledge, and attitudes. Healthcare providers must be self-directed learners to stay updated with medical advances and provide evidence-based patient care [34]. Therefore, an important responsibility of medical educators is to guide students to promote self-directed learning. ...
Background
Point-of-care ultrasound (POCUS) can be used in a variety of clinical settings and is a safe and powerful tool for ultrasound-trained healthcare providers, such as physicians and nurses; however, the effectiveness of ultrasound education for nursing students remains unclear. This prospective cohort study aimed to examine the sustained educational impact of bladder ultrasound simulation among nursing students.
Methods
To determine whether bladder POCUS simulation exercises sustainably improve the clinical proficiency regarding ultrasound examinations among nursing students, evaluations were conducted before and after the exercise and were compared with those after the 1-month follow-up exercise. A bladder urine volume measurement simulator and a portable ultrasound device were used during the exercise. Nursing student volunteers participated in this prospective observational study. The primary outcome was the Objective Structured Assessment of Ultrasound Skills (OSAUS) score. The secondary outcomes were the Self-Directed Learning Readiness Scale (SDLRS) and Student Satisfaction and Self-Confidence in Learning (SSSCLS) scores. Differences were analyzed using one-way analysis of variance with repeated measures.
Results
Data from 12 students were analyzed. The percentages of total OSAUS scores increased from 34.3% after the initial bladder POCUS simulation exercise to 51.0% after the 1-month follow-up exercise (p < 0.001). The OSAUS scores for several subdomains, including image optimization, systematic reviews, image interpretation, test documentation, and medical decision-making, increased significantly. In addition, the SDLRS significantly increased from 204.4 before the exercise to 233.6 after the 1-month follow-up exercise (p < 0.001), and the SSSCLS confidence scores also increased from 33.7 after the initial exercise to 36.4 after the 1-month follow-up exercise (p < 0.005).
Conclusion
The bladder POCUS simulation exercise is effective in continuously improving the clinical performance of nursing students for ultrasound examinations even at 1-month follow-up, increasing their confidence and promoting a self-directed learning attitude.
... These findings indicate that LLMs are moving closer to practical utility in nephrology education and clinical decision-making. Traditionally, clinicians have relied on textbooks, literature, lectures, and clinical training-yet the exponential growth of medical information makes it challenging to stay current [32]. LLMs like o1 pro may serve as ondemand knowledge resources, offering evidence summaries, restructured pathophysiological concepts, and assistance with image interpretation. ...
Background: Large language models (LLMs) are increasingly evaluated in medical education and clinical decision support, but their performance in highly specialized fields, such as nephrology, is not well established. We compared two advanced LLMs, GPT-4 and the newly released o1 pro, on comprehensive nephrology board renewal examinations.
Methods: We administered 209 Japanese Self-Assessment Questions for Nephrology Board Renewal from 2014-2023 to o1 pro and GPT-4 using ChatGPT pro. Each question, including images, was presented in separate chat sessions to prevent contextual carryover. Questions were classified by taxonomy (recall/interpretation/problem-solving), question type (general/clinical), image inclusion, and nephrology subspecialty. We calculated the proportion of correct answers and compared performances using chi-square or Fisher's exact tests.
Results: Overall, o1 pro scored 81.3% (170/209), significantly higher than GPT-4's 51.2% (107/209; p<0.001). o1 pro exceeded the 60% passing criterion every year, while GPT-4 achieved this in only two out of the ten years. Across taxonomy levels, question types, and the presence of images, o1 pro consistently outperformed GPT-4 (p<0.05 for multiple comparisons). Performance differences were also significant in several nephrology subspecialties, such as chronic kidney disease, confirming o1 pro's broad superiority.
Conclusion: o1 pro substantially outperformed GPT-4 in a comprehensive nephrology board renewal examination, demonstrating advanced reasoning and integration of specialized knowledge. These findings highlight the potential of next-generation LLMs as valuable tools in specialty medical education and possibly clinical support in nephrology, warranting further and careful validation.
Psychological disorders play a crucial yet often overlooked role in medical aesthetic disputes. Patients with underlying psychological conditions may have unrealistic expectations, leading to dissatisfaction and legal conflicts.
This review aims to explore the impact of psychological disorders on medical aesthetic disputes, analyze their prevalence, and discuss strategies to mitigate associated risks.
A comprehensive review of existing literature was conducted, focusing on the relationship between psychological disorders—such as body dysmorphic disorder (BDD), anxiety, and depression—and medical aesthetic disputes. Key factors influencing patient satisfaction and dispute resolution were examined.
Psychological disorders are significantly associated with higher dissatisfaction rates and increased risk of conflicts in aesthetic medicine. Inadequate psychological screening and poor communication between practitioners and patients contribute to these disputes. Implementing thorough psychological assessments and improving patient selection criteria can help reduce conflict and enhance treatment outcomes.
Understanding and addressing psychological disorders in aesthetic patients is essential for reducing disputes and improving patient satisfaction. Integrating mental health evaluations into aesthetic practice, along with better communication strategies, can help prevent conflicts and optimize clinical outcomes. Collaboration between aesthetic professionals and mental health specialists is recommended to ensure comprehensive patient care.
Level of Evidence IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.
This chapter overviews the diagnostic strategy for endometriosis, encompassing clinical symptoms, examination, imaging techniques, and biological markers. Within the realm of imaging techniques, particular attention is devoted to discussing the diagnostic efficacy of transvaginal ultrasonography (TVUS) and magnetic resonance imaging (MRI). Each modality’s strengths and limitations are thoroughly analyzed, elucidating the nuanced advantages and inconveniences associated with their use in diagnosing endometriosis.
Notably, MRI emerges as a notably sensitive tool, capable of discerning subtle pathological changes indicative of endometriosis, whereas TVUS exhibits commendable specificity in delineating anatomical details. The integration of these imaging modalities with clinical assessment holds promise for enhancing diagnostic accuracy.
Moreover, the article delves into the decision-making algorithms devised by prominent French and European medical societies, shedding light on the structured approaches employed by healthcare professionals in diagnosing endometriosis. Additionally, it offers specialized insights into tailoring diagnostic strategies to address the unique needs and challenges encountered in adolescent patients. Finally, this article provides a nuanced understanding of the diagnostic intricacies inherent in managing endometriosis across diverse patient populations.
Medical students face significant challenges retaining complex information, such as interpreting ECGs for coronary artery occlusions, amidst demanding curricula. While artificial intelligence (AI) is increasingly used for medical image analysis, this study explored using generative AI (DALLE-3) to create mnemonic-based images to enhance human learning and retention of medical images, in particular, electrocardiograms (ECGs). This study is among the first to investigate generative AI as a tool not for automated diagnosis but as a human-centered educational aid designed to enhance long-term retention in complex visual tasks like ECG interpretation. We conducted a comparative study with 275 first-year medical students across six campuses; an experimental group (n = 40) received a lecture supplemented with AI-generated mnemonic ECG images, while control groups (n = 235) received standard lectures with traditional ECG diagrams. Student achievement and retention were assessed by course examinations, and student preference and engagement were measured using the Situational Interest Survey for Multimedia (SIS-M). Control groups showed a significant decline in scores on the relevant exam question over time, whereas the experimental group’s scores remained stable, indicating improved long-term retention. Experimental students also reported significantly higher situational interest in the mnemonic-based images over traditional images. AI-generated mnemonic images can effectively improve long-term retention of complex ECG interpretation skills and enhance student engagement and preference, highlighting generative AI’s potential as a valuable cognitive tool in image analysis during medical education.
Large Language Models (LLMs) have shown considerable promise in knowledge processing and synthesis across various medical disciplines. In medical education, most applications have focused on comparing LLM outputs to trainee performance or using LLMs for standardized assessment. However, few studies have systematically evaluated the effects of standardized, LLM-powered, curricular interventions on medical learning.
This case study, conducted at The Warren Alpert Medical School of Brown University, assessed the impact of AI-generated Anki flashcards and lecture summaries specifically optimized for the pre-clerkship phase. These materials were developed using a rigorous, specific, and content-agnostic prompt engineering process and validated through standardized human grading to ensure both accuracy and relevance. The final prompts used demonstrated hallucination rates of 0 per summary and 1 per 21 flashcards and average coverage of 100% of faculty-identified learning objectives. Materials were given to students for two 3-week academic blocks, covering genetics and pharmacology.
Student exam scores and survey-based feedback were used to evaluate the effectiveness of these AI-generated resources. The study was conducted in a resource-rich pre-clerkship setting where students already have access to faculty-created materials, commercial content, and student-curated resources. We aimed to determine whether AI-generated content could offer measurable quantitative improvements or subjective qualitative benefits in a saturated learning environment.
Among participating first-year medical students, overall exam performance between those who used the AI-generated summaries and those who did not was comparable in both the genetics block (p = 0.76) and the pharmacology block (p = 0.35). Similarly, use of the AI-generated Anki flashcards was not associated with significant differences in exam scores for either genetics (p = 0.86) or pharmacology (p = 0.05). Qualitative analyses demonstrated widespread time saving for Anki flashcards (74%) and AI-generated summaries (61%), with 91% of users finding the custom AI-generated content more time-saving than default GPT-4o. There was a significant usage-dependent relationship of higher AI-usage correlating with increased agreement of equivalency or utility over faculty-generated lecture notes (Pearson's r2=0.55) and student-created flashcards (Pearson's r2=0.79).
These findings suggest that students who used AI-generated content maintained comparable educational outcomes in the pre-clerkship setting. Moreover, subjective perceptions among learners, such as time saved and content usefulness, highlight the potential value of LLM-powered tools when layered on top of an existing well-resourced curricular structure. Future work will examine the benefits of this work in less structured medical education settings, such as clinical and surgical education.
This commentary by a patient describes a syndrome that often develops residual to chemotherapy and yet escapes timely detection and effective treatment: chemo-induced peripheral neuropathy (CIPN). Physicians and their patients who suffer from CIPN, particularly those with unremitting small fiber neuropathy (SFN), may benefit from the evidence-based guidance provided. The author also calls for deeper listening by the medical community, interdisciplinary collaboration and patient self-advocacy.
Preventive medicine targets various stages of chronic disease progression, yet its widespread implementation is hindered by gaps in medical education. Integrating preventive and lifestyle medicine into curricula can bridge these gaps, enhancing the healthcare system. This holistic approach addresses the root causes of diseases related to lifestyle factors such as nutrition, physical activity, stress, sleep, and substance use. Emphasizing preventive care in education empowers professionals to tackle healthcare challenges proactively. Stakeholders must prioritize and implement comprehensive preventive medicine programs, fostering collaborative efforts at both individual and community levels for sustainable well-being.
ChatGPT, a comprehensive language processing model, provides the opportunity for supportive and professional interactions with patients. However, its use to address patients’ frequently asked questions (FAQs) and the readability of the text generated by ChatGPT remain unexplored, particularly in geriatrics. We identified the FAQs about common geriatric syndromes and assessed the accuracy and readability of the responses provided by ChatGPT.
Two geriatricians with extensive knowledge and experience in geriatric syndromes independently reviewed the 28 responses provided by ChatGPT. The accuracy of the responses generated by ChatGPT was categorized on a rating scale from 0 (harmful) to 4 (excellent) based on current guidelines and approaches. The readability of the text generated by ChatGPT was assessed by administering two tests: the Flesch–Kincaid Reading Ease (FKRE) and the Flesch–Kincaid Grade Level (FKGL).
ChatGPT-generated responses with an overall mean accuracy score of 88% (3.52/4). Responses generated for sarcopenia diagnosis and depression treatment in older adults had the lowest accuracy scores (2.0 and 2.5, respectively). The mean FKRE score of the texts was 25.2, while the mean FKGL score was 14.5.
The accuracy scores of the responses generated by ChatGPT were high in most common geriatric syndromes except for sarcopenia diagnosis and depression treatment. Moreover, the text generated by ChatGPT was very difficult to read and best understood by college graduates. ChatGPT may reduce the uncertainty many patients face. Nevertheless, it remains advisable to consult with subject matter experts when undertaking consequential decision-making.
This chapter examines the overwhelming increase in medical information and its impact on healthcare professionals, detailing the rapid doubling of medical knowledge and the vast amount of data produced by the healthcare industry. It highlights the significant cognitive burden on doctors, the challenges in keeping up with medical literature, and the staggering volume of healthcare data that remains largely untapped. The chapter also addresses the grave consequences of medical errors worldwide, the global healthcare workforce crisis, and the escalating healthcare costs without improved outcomes. It underscores the healthcare sector’s interconnected challenges, including the data explosion, high rates of medical errors, workforce shortages, and the pressures of an aging population on healthcare systems. The piece advocates for integrating Artificial Intelligence (AI) as a solution to navigate these challenges, emphasizing AI’s role in enhancing healthcare outcomes, reducing errors, and managing the data deluge. It explores the current state of AI in healthcare, its potential to transform patient care through advanced diagnostics, personalized medicine, and the development of multimodal AI systems for a holistic view of patient health. The chapter concludes by highlighting the imperative of embracing AI in healthcare as a pivotal step towards addressing current and future challenges, calling for collective action to understand and integrate AI technologies for a sustainable healthcare future.
Medical education has undergone a significant reform in response to evolving health care demands, technological advancements, and emerging research in biomedical and education sciences. The teaching methods in anatomy, a core component of medical education, have notably changed, with traditional cadaveric dissection being increasingly replaced by digital and hybrid alternatives. Despite research indicating no significant difference in short-term knowledge retention between students who engaged in cadaveric dissection and those engaging in alternative methods, dissection uniquely fosters professionalism, empathy, and ethical awareness - traits essential for holistic medical education. This review critically examines the dichotomy between traditional and innovative teaching methods in anatomy education, questioning the assumption that traditional methods hinder progress in modern health care. The findings suggest that changes in medical education are primarily influenced by organizational issues, which frequently results in an incomplete implementation of innovative teaching approaches. The inconsistent application of innovative teaching methods makes it difficult to assess their effectiveness and compare them with traditional methods. Reliable data on their long-term impact can only be generated by randomized controlled trials and longitudinal studies. In the meantime, we need to ensure that current medical students receive high-quality education by incorporating best practices from diverse teaching methods based on valuable insights from experienced educators and current students' learning preferences.
The global decline in the number of physician-scientists, despite an increase in practicing physicians, underscores a critical need for integrating research training into medical education. Addressing this issue, we established an international research exchange program between the University of Tartu (UT) and the University of Iceland (UI). This initiative aimed to enhance scientific literacy, foster transferable skills, and align curricula with European standards through collaborative research experiences.
The program enabled reciprocal student mobility, involving 11 medical undergraduates (6 from UT, 5 from UI), who conducted month-long basic science research projects. Participants completed comprehensive pre-training in scientific communication, safety protocols, and ethics before earning 6 ECTS credits for fulfilling laboratory requirements and submitting final reports. Students were also required to participate in local public engagement events. Despite challenges and delays due to the COVID-19 pandemic, the program met its objectives, demonstrating adaptability and effective resource management. Key outcomes included the development of an online learning platform, and multilingual guidelines and validated survey instruments to evaluate program impact, which we provide here to support similar initiatives. Feedback from pre-, post-, and post-1-year questionnaires revealed significant improvements in participants confidence in research methodologies, critical appraisal of scientific literature, and motivation for future research involvement. This project highlights the potential of structured international exchange programs to address gaps in medical education, enhance scientific training, and cultivate the next generation of physician-scientists.
This work demonstrates how and why the expertise of non‐medical actors, such as patients, caregivers, and patient advocacy groups, is essential for improving patient outcomes and quality of life. ‘Vernacular’ knowledge, including community beliefs, values, networks, and practices, both challenge and collaborate with institutional scientific knowledge. The value of this interaction of multiple kinds of knowledge is illuminated through a study of spinal muscular atrophy in Colombia, which explores how mother caregivers develop unique expertise, devices, and care practices, and use online social networks to share and learn both clinical and vernacular information. Supported by a broad body of evidence and theory, this paper makes a powerful case for elevating vernacular knowledge and the people and communities who practice it. It also showcases methods and channels that researchers and medical professionals can use to collaborate with caregivers in these non‐institutional, but medically essential, spaces.
Objectives
Currently, there is no generally accepted consensus on how to translate strength of evidence into language. Against this background, we here investigated how widely used verbal descriptors of evidence grades and clinical practice recommendations, respectively, are understood, interpreted and transferred into intentions to treat. We explored differences between medical specialists and undergraduate medical students.
Design
Cross-sectional, anonymous online survey. Assessment was based on publicly available proposals for wording to characterise strength of evidence from randomised versus non-randomised trials and of clinical practice recommendations, respectively.
Setting
The online survey was conducted between September 2021 and March 2022 and promoted by several professional organisations in German-speaking European countries (Germany, Austria and Switzerland).
Participants
Medical students, trainees and medical specialist (open to all medical specialties).
Outcome
The survey was composed of two sections: (1) Aim of the first survey section was to assess if the linguistic differentiation between results from randomised versus non-randomised studies is correctly understood as put forward by the proponents. To this end, participants were asked to grade the relative weight of the expression for the results of a randomised trial versus two proposals for how to express the results of non-randomised studies. (2) Next, strong positive, weak positive, weak negative and strong negative clinical recommendations were presented, and participants were asked to answer in a forced-choice format if they would treat all/no patients, selected patients or only consider treatment. Additionally, the number of eligible patients who would be treated was assessed.
Results
N=1081 physicians and N=539 medical students completed the survey. (1) Less than half of the participants (48.5%) interpreted use of ‘associated with’ as linguistic differentiation between results from randomised versus non-randomised trials in a sense as put forward by the proponents. However, use of the subjunctive mood (‘could’) resulted in 87.3% correct differentiations. (2) Even with only four types of clinical practice recommendations (positive/negative and strong/weak), interpretation and translation into intention to treat, respectively, showed a heterogeneous picture: while the presentation of a strong clinical recommendation led to largely congruent responses, the interpretation of weak recommendations showed a high variability, with no clear response pattern for intentions to treat. Responses from physicians and medical students were largely comparable.
Conclusion
This study demonstrates limitations in the currently used linguistic expressions of strength of evidence and clinical practice recommendations and supports the need to prospectively test effects of language on intentions to treat prior to implementation of a certain wording. Study results cast doubt on that linguistic means alone will lead to optimally targeted intentions to treat.
The transformation of medical education as a consequence of the expansion of scientific knowledge, epidemiological risks such as the COVID-19 pandemic, as well as the incorporation of emerging technology in health care, have been shown to be determining factors of the context and origin of the challenges facing the training of the doctors of the future today. Therefore, medical education must be aimed at ensuring that future doctors not only consume the knowledge at their disposal, but also criticize it, produce it and renew it. Our times require leaders in healthcare who have the courage to point out the need to change strategies, who do not hesitate to correct their mistakes and take the risk to guide the right path for the future. Thus, medical education sees the need to develop in future doctors the ability to work as a team for the benefit of patients and the health systems in which they operate. Therefore, the greatest challenge facing the transformation of medical education at a global level is to encompass greater correspondence between the educational structure of medical schools and faculties and the medical service provided to the world population.
The quick progressions in artificial intelligence (AI) have changed different industries, and medical care is no special case. AI has emerged as a potent instrument for enhancing efficiency, enhancing patient care, and optimizing hospital workforce management in recent years. This chapter investigates the combination of AI into medical clinic work force the board frameworks and its suggestions for the medical services industry.
The section starts by giving an outline of the difficulties looked by medical care establishments in dealing with their work force actually. Staff shortages, complicated scheduling, optimizing skill mix, and maintaining high-quality patient care are among these obstacles. The development of AI-driven solutions presents promising avenues for addressing these issues.
The chapter then delves into the various ways AI can be used to manage hospital staff. It investigates how historical data can be analyzed by AI-powered algorithms to forecast patient demand and adjust staffing levels accordingly. It also looks into AI-driven scheduling systems that take into account staff preferences, qualifications, and workload distribution, resulting in higher levels of job satisfaction and work-life balance. The role of AI in skill mix optimization is also discussed, where machine learning algorithms help identify the most suitable staff members for specific tasks based on their expertise and experience. Furthermore, the chapter explores how AI can contribute to workforce development by identifying skill gaps and recommending training programs tailored to individual staff members and needs.
Ethical considerations surrounding the implementation of AI-managed hospital workforces are thoroughly examined. The chapter discusses the importance of transparency, data privacy, and the need for human oversight in AI-powered systems to maintain patient safety and ensure equitable treatment for all.
Additionally, the chapter touches upon the challenges and potential resistance faced during the integration of AI into workforce management practices. Cultural acceptance, staff training, and change management strategies are highlighted as crucial factors in successful AI adoption.
Finally, the chapter concludes by discussing the future prospects of AI-managed hospital workforces. It envisions a landscape where AI continues to evolve, leading to more sophisticated applications in healthcare workforce management, ultimately revolutionizing the way hospitals operate and deliver care.
Overall, the chapter provides useful insights into the transformative potential of AI in workforce management in hospitals. It fills in as an exhaustive asset for medical services experts, policymakers, and specialists trying to grasp the effect of simulated intelligence in moulding the fate of medical care conveyance.
Ortopedi 4.0: Dijitalleşme, Yapay Zeka ve Yenilikçi Teknolojiler Nihat Demirhan DEMİRKIRAN Ramadan ÖZMANEVRA Geleceğin Ortopedisi: Dijitalleşme ve Yapay Zekanın Rolü Kadir GÜLNAHAR Ortopedide Dijitalleşmenin Tarihçesi ve Geleceği Burak YILDIRIM Kişiselleştirilmiş Ortopedik Tedavi İlyas KABAN Hasta Sonuçlarını İyileştirmek İçin Yapay Zekâ Kullanımı Ahmet ACAR Ortopedik Acil Durumlarda Dijital Çözümler ve Yapay Zeka Destekli Müdahaleler Cengiz Han KANTAR Ortopedik Tedavide 3 Boyutlu Baskı ve Yapay Zeka İdris PERKTAŞ Chatgpt İle Ortopedik Tanı ve Tedavi Süreçlerinde Destek Mehmet Yiğit GÖKMEN Chatgpt’nin Ortopedik Hasta Takibinde Kullanımı: Olgu Sunumları ve Örnekler Ömer TORUN Ortopedik Eğitimde Dijital Araçlar ve Simülasyonlar Cengiz Han KANTAR Ortopedik Hastaların Bilgilendirmesinde Chatgpt’nin Rolü Eren BULUT Robotik Cerrahi ve Ortopedi: Yeni Nesil Cerrahlar Tahir Burak SARITAŞ
Although biomedical knowledge is believed to be of little value in diagnosis of routine clinical cases, studies of clinical reasoning have found that physicians revert to use of basic biomedical knowledge when faced with challenging clinical problems. The current paper presents two experiments that empirically examine the role of biomedical knowledge in diagnosis of difficult cases by novice diagnosticians. Novices are taught to diagnose a series of artificial diseases using either knowledge of causal mechanisms or a list of clinical features. In Experiment 1, participants are then tested on two types of clinical challenges: (1) case summaries with irrelevant findings; (2) cases using unfamiliar terminology. Participants with an understanding of underlying mechanisms out performed their counterparts on both types of cases. In Experiment 2, participants are tested 1 week after initial training. Participants with knowledge of causal mechanisms were found to do better on cases with unfamiliar terminology. The results of the two studies provide additional support for the critical role of biomedical knowledge in diagnosis of difficult clinical cases.
Although there is consensus among medical educators that students must receive training in the biomedical sciences, little is known regarding the role of biomedical knowledge in diagnosis. The present paper presents two studies examining the role of biomedical knowledge, specifically knowledge of causal mechanisms, in novice diagnosticians. In Experiment 1, two groups of participants are taught to diagnose a series of artificial diseases. In the causal learning condition students learn the underlying causal mechanisms for each feature. A second group learns the same diseases without the causal explanations. Participants are asked to diagnose a series of written cases immediately after training and again 1 week later. The results show that students who learn a causal model are better able to retain their diagnostic performance over time (89% correct vs. 78%). This finding is investigated further in Experiment 2, demonstrating that students rely more on casual information after a delay (mean probability of 57% vs. 43%). Together, the studies suggest that knowledge of underlying causal mechanisms can aid student memory for diagnostic categories and that use of causal knowledge changes over time.
Released in 1910, The Flexner Report (Medical Education in the United States and Canada) changed the face of medical education in the United States. Commissioned by the Carnegie Foundation for the Advancement of Teaching at the request of the American Medical Association's Council on Medical Education, Abraham Flexner collected information about the current state of medical education in the United States and Canada by visiting all 155 medical schools in North America. His report highlighted several university-based programs that had already adopted a system firmly rooted in scientific inquiry and concomitantly called for extensive reform and standardization of the medical education system. As a result of this report, many medical schools considered substandard were closed, medical licensing and certification procedures were developed, and the general organization of medical education (2 years of preclinical courses followed by 2 years of clinical training) was firmly established.
100 years ago, a series of studies about the education of health professionals sparked groundbreaking reforms. The challenges of the 21st century demand a new redesign of professional health education. The Commission on Education of Health Professionals for the 21st Century came together to develop a shared vision and a common strategy for postsecondary education in medicine, nursing, and public health. The Commission provides a vision that calls for a new era of professional education that advances transformative learning and harnesses the power of interdependence in education. Just as reforms in the early 20th century were advanced by the germ theory and the establishment of the modern medical sciences, so too the Commission believes that the future will be shaped by adaptation of competencies to specific contexts drawing on the power of global flows of information and knowledge. Undertaking of this vision requires a series of instructional and institutional reforms, which are guided by the two expected outcomes, transformative learning and interdependence in education. On the basis of these core notions, the Commission offers 10 specific recommendations. The implementation of these recommendations require a series of enabling actions, including the mobilization of leadership, the enhancement of investments in health education, the alignment of the accreditation processes, and the strengthening of global learning. These recommendations also demand the support of a global movement engaging all stakeholders as part of a concerted effort to strengthen health systems.
This presentation shall discuss the major "internal" challenges to medical education--that is, challenges to achieving effective medical teaching that have arisen from the evolution of the science and practice of medicine. These issues are defined as "internal" in that they reflect the internal evolution of the profession and of academic medical centers, independent of the perturbations in medical education we are presently experiencing as a result of the hostile external environment of health care. Examples of these internal challenges include the growing "bench-bedside gap," the traditional tensions at medical schools between teaching and research, and the need to adjust medical education to meet the challenges imposed by chronic diseases. The need for "internal" leadership from within the profession to help solve some of the "external" problems of medical education shall also be discussed.
The traditional approach to clinical education has involved a series of rotations in various clinical settings. This article which concludes the medical education series, presents evidence and musters arguments for longitudinal integration of the medical curriculum.
In the 117th Shattuck Lecture, Dr. Steven Schroeder asks why the American system fails to deliver a standard of health similar to that observed in many other countries. In his arguments, he focuses on the public health risks of smoking and obesity and how they have been managed.
Revisiting the Medical School Mission at a Time of Expansion
- Jj Cohen
- Hager
- Russell
Cohen JJ, Hager M and Russell S. Revisiting the Medical School Mission at a Time of Expansion. New York: Josiah Macy, Jr. Foundation; 2009.