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Artificial Intelligence and Machine Learning May Resolve Health Care Information Overload
Artificial intelligence (AI) with its diverse domains such as expert systems and machine learning already has multiple potential applications in medicine.
Based on the latest developments in the multifaceted field of AI, it will play a pivotal role in medicine, with a high transformative potential in multiple areas, including drug development, diagnostics, patient care and monitoring.
In the pharmaceutical industry AI is also rapidly gaining a crucial role. The introduction of innovative medicines requires profound background knowledge and the latest means of communication. This drives us to intensively engage with the topic of medical education, which is becoming more and more demanding due to the dynamic knowledge landscape, among other things, accelerated even more by digitalization and AI.
Therefore, we argue for the incorporation of AI-based tools and methods in medical education, including personalized learning, diagnostic pathways, and data analysis, to prepare healthcare professionals for the evolving landscape of AI in medicine and support the fluency in dealing with AI by regular contact with various AI-based tools (Learning with AI).
Understanding AI's vast potential and its caveats as well as gaining a basic knowledge of how AI works should be an important part of medical education to ensure that physicians can effectively and responsibly leverage AI-based systems in their daily practice and in scientific communication (Learning about AI).
In this commentary, we discuss ChatGPT and our perspectives on its utility to systematic reviews (SRs) through the appropriateness and applicability of its responses to SR related prompts. The advancement of artificial intelligence (AI)-assisted technologies leave many wondering about the current capabilities, limitations, and opportunities for integration AI into scientific endeavors. Large language models (LLM)—such as ChatGPT, designed by OpenAI—have recently gained widespread attention with their ability to respond to various prompts in a natural-sounding way. Systematic reviews (SRs) utilize secondary data and often require many months and substantial financial resources to complete, making them attractive grounds for developing AI-assistive technologies. On February 6, 2023, PICO Portal developers hosted a webinar to explore ChatGPT’s responses to tasks related to SR methodology. Our experience from exploring the responses of ChatGPT suggest that while ChatGPT and LLMs show some promise for aiding in SR-related tasks, the technology is in its infancy and needs much development for such applications. Furthermore, we advise that great caution should be taken by non-content experts in using these tools due to much of the output appearing, at a high level, to be valid, while much is erroneous and in need of active vetting.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13643-023-02243-z.
Background:
Artificial intelligence (AI) has advanced substantially in recent years, transforming many industries and improving the way people live and work. In scientific research, AI can enhance the quality and efficiency of data analysis and publication. However, AI has also opened up the possibility of generating high-quality fraudulent papers that are difficult to detect, raising important questions about the integrity of scientific research and the trustworthiness of published papers.
Objective:
The aim of this study was to investigate the capabilities of current AI language models in generating high-quality fraudulent medical articles. We hypothesized that modern AI models can create highly convincing fraudulent papers that can easily deceive readers and even experienced researchers.
Methods:
This proof-of-concept study used ChatGPT (Chat Generative Pre-trained Transformer) powered by the GPT-3 (Generative Pre-trained Transformer 3) language model to generate a fraudulent scientific article related to neurosurgery. GPT-3 is a large language model developed by OpenAI that uses deep learning algorithms to generate human-like text in response to prompts given by users. The model was trained on a massive corpus of text from the internet and is capable of generating high-quality text in a variety of languages and on various topics. The authors posed questions and prompts to the model and refined them iteratively as the model generated the responses. The goal was to create a completely fabricated article including the abstract, introduction, material and methods, discussion, references, charts, etc. Once the article was generated, it was reviewed for accuracy and coherence by experts in the fields of neurosurgery, psychiatry, and statistics and compared to existing similar articles.
Results:
The study found that the AI language model can create a highly convincing fraudulent article that resembled a genuine scientific paper in terms of word usage, sentence structure, and overall composition. The AI-generated article included standard sections such as introduction, material and methods, results, and discussion, as well a data sheet. It consisted of 1992 words and 17 citations, and the whole process of article creation took approximately 1 hour without any special training of the human user. However, there were some concerns and specific mistakes identified in the generated article, specifically in the references.
Conclusions:
The study demonstrates the potential of current AI language models to generate completely fabricated scientific articles. Although the papers look sophisticated and seemingly flawless, expert readers may identify semantic inaccuracies and errors upon closer inspection. We highlight the need for increased vigilance and better detection methods to combat the potential misuse of AI in scientific research. At the same time, it is important to recognize the potential benefits of using AI language models in genuine scientific writing and research, such as manuscript preparation and language editing.
Large Language Models (LLMs) are a key component of generative artificial intelligence (AI) applications for creating new content including text, imagery, audio, code, and videos in response to textual instructions. Without human oversight, guidance and responsible design and operation, such generative AI applications will remain a party trick with substantial potential for creating and spreading misinformation or harmful and inaccurate content at unprecedented scale. However, if positioned and developed responsibly as companions to humans augmenting but not replacing their role in decision making, knowledge retrieval and other cognitive processes, they could evolve into highly efficient, trustworthy, assistive tools for information management. This perspective describes how such tools could transform data management workflows in healthcare and medicine, explains how the underlying technology works, provides an assessment of risks and limitations, and proposes an ethical, technical, and cultural framework for responsible design, development, and deployment. It seeks to incentivise users, developers, providers, and regulators of generative AI that utilises LLMs to collectively prepare for the transformational role this technology could play in evidence-based sectors.
Background
Artificial intelligence (AI) has shown promising results in various fields of medicine. It has the potential to facilitate shared decision making (SDM). However, there is no comprehensive mapping of how AI may be used for SDM.
Objective
We aimed to identify and evaluate published studies that have tested or implemented AI to facilitate SDM.
Methods
We performed a scoping review informed by the methodological framework proposed by Levac et al, modifications to the original Arksey and O'Malley framework of a scoping review, and the Joanna Briggs Institute scoping review framework. We reported our results based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) reporting guideline. At the identification stage, an information specialist performed a comprehensive search of 6 electronic databases from their inception to May 2021. The inclusion criteria were: all populations; all AI interventions that were used to facilitate SDM, and if the AI intervention was not used for the decision-making point in SDM, it was excluded; any outcome related to patients, health care providers, or health care systems; studies in any health care setting, only studies published in the English language, and all study types. Overall, 2 reviewers independently performed the study selection process and extracted data. Any disagreements were resolved by a third reviewer. A descriptive analysis was performed.
Results
The search process yielded 1445 records. After removing duplicates, 894 documents were screened, and 6 peer-reviewed publications met our inclusion criteria. Overall, 2 of them were conducted in North America, 2 in Europe, 1 in Australia, and 1 in Asia. Most articles were published after 2017. Overall, 3 articles focused on primary care, and 3 articles focused on secondary care. All studies used machine learning methods. Moreover, 3 articles included health care providers in the validation stage of the AI intervention, and 1 article included both health care providers and patients in clinical validation, but none of the articles included health care providers or patients in the design and development of the AI intervention. All used AI to support SDM by providing clinical recommendations or predictions.
Conclusions
Evidence of the use of AI in SDM is in its infancy. We found AI supporting SDM in similar ways across the included articles. We observed a lack of emphasis on patients’ values and preferences, as well as poor reporting of AI interventions, resulting in a lack of clarity about different aspects. Little effort was made to address the topics of explainability of AI interventions and to include end-users in the design and development of the interventions. Further efforts are required to strengthen and standardize the use of AI in different steps of SDM and to evaluate its impact on various decisions, populations, and settings.
Purpose:
The purpose of this study was to develop a deep learning model to accurately detect anterior cruciate ligament (ACL) ruptures on magnetic resonance imaging (MRI) and evaluate its effect on the diagnostic accuracy and efficiency of clinicians.
Methods:
A training dataset was built from MRIs acquired from January 2017 to June 2021, including patients with knee symptoms, irrespective of ACL ruptures. An external validation dataset was built from MRIs acquired from January 2021 to June 2022, including patients who underwent knee arthroscopy or arthroplasty. Patients with fractures or prior knee surgeries were excluded in both datasets. Subsequently, a deep learning model was developed and validated using these datasets. Clinicians of varying expertise levels in sports medicine and radiology were recruited and their capacities in diagnosing ACL injuries in terms of accuracy and diagnosing time were evaluated both with and without artificial intelligence (AI) assistance.
Results:
A deep learning model was developed based on the training dataset of 22767 MRIs from 5 centers, and verified with external validation dataset of 4086 MRIs from 6 centers. The model achieved an area under the receiver operating characteristic curve of 0.980 and a sensitivity and specificity of 95.1%. Thirty-eight clinicians from 25 centers were recruited to diagnose 3800 MRIs. The AI assistance significantly improved the accuracy of all clinicians, exceeding 96%. Additionally, a notable reduction in diagnostic time was observed. The most significant improvements in accuracy and time efficiency were observed in the trainee groups, suggesting that AI support is particularly beneficial for clinicians with moderately limited diagnostic expertise.
Conclusions:
This deep learning model demonstrated expert-level diagnostic performance for ACL ruptures, serving as a valuable tool to assist clinicians of various specialties and experience levels in making accurate and efficient diagnoses.
Purpose:
The purpose of this study was to analyse the quality and readability of information regarding shoulder stabilisation surgery available using an online AI software (ChatGPT), using standardised scoring systems, as well as to report on the given answers by the AI.
Methods:
An open AI model (ChatGPT) was used to answer 23 commonly asked questions from patients on shoulder stabilization surgery. These answers were evaluated for medical accuracy, quality and readability using The JAMA Benchmark criteria, DISCERN score, Flesch-Kincaid Reading Ease Score (FRES) & Grade Level (FKGL).
Results:
The JAMA Benchmark criteria score was 0, which is the lowest score indicating no reliable resources cited. The DISCERN score was 60, which is considered a good score. The areas that open AI model did not achieve full marks were also related to the lack of available source material used to compile the answers, and finally some shortcomings with information not fully supported by the literature. The FRES was 26.2, and the FKGL was considered to be that of a college graduate.
Conclusion:
There was generally high quality in the answers given on questions relating to shoulder stabilization surgery, but there was a high reading level required to comprehend the information presented. However, but it is unclear where the answers came from with no source material cited. Although, it is important to note that the ChatGPT software repeatedly reference the need to discuss these questions with an orthopaedic surgeon and the importance of shared discussion making, as well as compliance with surgeon treatment recommendations.
As the implementation of artificial intelligence in orthopedic surgery research flourishes, so grows the need for responsible use. Related research requires clear reporting of algorithmic error rates. Recent studies show that preoperative opioid use, male sex, and greater body mass index are risk factors for extended, postoperative opioid use, but may result in high false positive rates. Thus, to be applied clinically when screening patients, these tools require physician and patient input, and nuanced interpretation, as the utility of these screening tools diminish without providers interpreting and acting on the information. Machine learning and artificial intelligence should be viewed as tools that can facilitate these human conversations among patients, orthopedic surgeons, and health care providers.
Certain types of scientific articles, including bibliographic articles, systematic reviews, and meta-analyses, require systematic search of electronic databases. Literature must be searched using clearly specified search terms, dates, and algorithms; article inclusion and exclusion criteria; and explicitly named databases. Search methods must be described in detail to allow reproducibility. In addition, responsibilities of all authors include contributing to study conception, design, data acquisition, analysis or interpretation; drafting or critically revising the work; approving the final version to be published; being accountable for the accuracy and integrity; being available to respond to queries including after publication; being able to identify which co-authors are responsible for which parts; and maintaining primary data and underpinning analysis for at least 10 years. The responsibilities of authorship are vast.
With genuine gratitude to the AANA Education Foundation for their unstinting support, it is our honor to announce Arthroscopy's Annual Awards for the best Clinical Research, Basic Science Research, Resident/Fellow Research, and Systematic Reviews published in 2022, as well as the Most Downloaded and Most Cited papers published 5 years ago. And as is customary in January, our editors update their disclosures of potential conflicts of interest, as we require of authors, and we update our masthead to introduce new members of our Editorial Board and Social Media Board.
Arthroscopy; Arthroscopy Techniques; and Arthroscopy, Sports Medicine, and Rehabilitation (ASMAR) websites include content that is available only online. Every time we visit the websites, we discover new content and educational features worth exploring. From meeting abstracts to multimedia, and from research pearls collections to world maps indicating the reach of our journals, a tour of our websites is enthralling. You can even take a bite of a hamburger.
Machine learning, a subset of artificial intelligence, has become increasingly common in the analysis of orthopaedic data. The resources needed to utilize machine-learning approaches for data analysis have become increasingly accessible to researchers, contributing to a recent influx of research using these techniques. As machine learning becomes increasingly available, misapplication owing to a lack of competence becomes more common. Sensationalized titles, misused vernacular, and a failure to fully vet machine learning–derived algorithms are just a few issues that warrant attention. As the orthopaedic community’s knowledge on this topic grows, the flaws in our understanding of this field will likely become apparent, allowing for rectification and ultimately improvement of how machine learning is utilized in research.
In 2010, our editorial team wrote about the Internet’s inarguable role in overloading information on our readers. In this editorial, we reflect on insights gained, mostly in the past decade, regarding the Internet and social media. Medical and surgical information online is easy to obtain, but it varies from platform to platform, is low in quality and reliability, and overestimates the public’s ability to decipher the information. Physicians do not use social media enough, or well. Social media can engage patients and can inform patients about the quality of medical and surgical information online. Physicians, themselves, can provide reliable information that informs patients and eases their minds. Physician-authors can use social media to develop communities with shared interests in research; members of these communities can post research findings and highlight the publications in which they find them. Discussion of research online increases the likelihood that it will be cited. It is no surprise that the Internet and social media have contributed to the growth of Arthroscopy; Arthroscopy Techniques; and Arthroscopy, Sports Medicine, and Rehabilitation.
Machine learning (ML) and artificial intelligence (AI) may be described as advanced statistical techniques using algorithms to “learn” to evaluate and predict relationships between input and results without explicit human programming, often with high accuracy. The potentials and pitfalls of ML continue to be explored as predictive modeling grows in popularity. While use of and optimism for AI continues to increase in orthopaedic surgery, there remains little high-quality evidence of its ability to improve patient outcome. It is up to us as clinicians to provide context for ML models and guide the use of these technologies to optimize the outcome for our patients. Barriers to widespread adoption of ML include poor quality data, limits to compliant data sharing, few clinicians who are expert in ML statistical techniques, and computing costs including technology, infrastructure, personnel, energy, and updates.
There exists great hope and hype in the literature surrounding applications of artificial intelligence (AI) to orthopaedic surgery. Between 2018-2021, a total of 178 AI-related articles were published in orthopaedics. However, for every two original research papers that apply AI to orthopaedics, a commentary or review is published (30.3%). AI-related research in orthopaedics frequently fails to provide use cases that offer the uninitiated an opportunity to appraise the importance of AI by studying meaningful questions, evaluating unknown hypotheses, or analyzing quality data. The hype perpetuates a feed-forward cycle that relegates AI to a meaningless buzzword by rewarding those with nascent understanding and rudimentary technical knowhow into committing several basic errors: (1) inappropriately conflating vernacular (“AI/ML”), (2) repackaging registry data, (3) prematurely releasing internally validated algorithms, (4) overstating the “black box phenomenon” by failing to provide weighted analysis, (5) claiming to evaluate AI rather than the data itself, and (6) withholding full model architecture code. Relevant AI-specific guidelines are forthcoming, but forced application of the original TRIPOD guidelines designed for regression analyses are irrelevant and misleading. To safeguard meaningful use, AI-related research efforts in orthopaedics should be (1) directed towards administrative support over clinical evaluation and management, (2) require the use of the advanced model, and (3) answer a question that was previously unknown, unanswered, or unquantifiable.
With the plethora of machine learning (ML) analyses published in the orthopaedic literature within the last five years, several attempts have been made to enhance our understanding of what exactly ML means and how it is used. At its most fundamental level, ML comprises a branch of artificial intelligence that uses algorithms to analyze and learn from patterns in data without explicit programming or human intervention. On the contrary, traditional statistics require a user to specifically choose variables of interest to create a model capable of predicting an outcome, the output of which (1) may be falsely influenced by the variables chosen to be included by the user and (2) does not allow for optimization of performance. Early publications have served as succinct editorials or reviews intended to ease audiences unfamiliar with ML into the complexities that accompany the subject. Most commonly, the focus of these studies concerns the terminology and concepts surrounding ML as it is important to understand the rationale behind performing such studies. Unfortunately, these publications only touch on the most basic aspects of ML and are too frequently repetitive. Indeed, the conclusion of these articles reiterate that the potential clinical utility of these algorithms remain tangential at best in their current form and caution against premature adoption without external validation. By doing so, our perspective and ability to draw our own conclusions from these studies has not advanced, and we are left concluding with each subsequent study that a new algorithm is published for an outcome of interest that cannot be used until further validation. What readers now need is to regress to embrace the principles of the scientific method that they have used to critically assess vast numbers of publications prior to this wave of newly applied statistical methodology – a guide to interpret results such that their own conclusions can be drawn.
Recent research using machine learning and data mining to determine predictors of prolonged opioid use after arthroscopic surgery showed that Artificial Neural Networks showed superior discrimination and calibration. Other machine learning algorithms, such as Naïve Bayes, XG Boost, Gradient Boosting Model, Random Forest, and Elastic Net, were also reliable despite slightly lower Brier scores and mean areas under the curve. Machine learning and data mining have limitations, however, and outputs are reliant on large sample sizes and the accuracy of big data. Poor-quality data and the lack of confounding variables are further limitations. There is no doubt that predictive modeling, artificial intelligence, machine learning, and data mining will become a major component of the physician’s practice, and doctors of medicine and related researchers should become familiar with these techniques. Physicians require an understanding of data science for the following reasons: monitoring of large databases could allow early diagnosis of pathologic conditions in individual patients; multiparameter data can be used to assist in the development of care pathways; data visualization could help with interpretation of medical images; understanding artificial intelligence workflow and machine learning will help us with understanding early warning signs of disease; and data science will facilitate personalized medicine with which clinicians can predict treatment outcomes.
With sincere appreciation to the AANA Education Foundation for their generous support, we announce our Annual Awards for the best Clinical Research, Basic Science Research, Resident/Fellow Research, and Systematic Reviews published in 2021, as well as the Most Downloaded and Most Cited papers published 5 years ago. Also, as is customary and as we require of authors, our editors update their annual disclosures of potential conflicts of interest. Finally, we annually update our masthead, thus introducing a new Associate Editor and many new members of the Editorial Board and Social Media Board.
The “imprimatur” of peer review signifies that medical journal reviewers and editors have peer reviewed the submitted work of authors and suggested opportunities for authors to make revisions that will improve their submission, allowing publication. Work that meets the editorial standards for publication after revision receives the imprimatur, a proverbial stamp of approval.
In summary, AI and machine learning are new to Arthroscopy journal, and absent background, the concepts and related research may seem confusing and unapproachable. The fact that AI and machine learning refer to computers designed by humans reminds us of the following: AI and machine learning represent tools to which we, as clinicians and scientists, must adapt. Next, because machine learning is a type of AI in which computers are programmed to improve the algorithms under which they function over time, insight is required to achieve an element of explainability regarding the key data underlining a particular machine-learning prediction. Finally, machine-learning algorithms require validation before they can be applied to data sets different from the data on which they were trained.
Virtual reality (VR) simulation has enormous potential utility in technically demanding manual activities. Hip arthroscopy is a perfect example of a challenging surgical technique with an extensive learning curve. The literature has recently consistently demonstrated that both career and annual maintenance case volume significantly influences patient-reported outcomes and risk of revision surgery and complications. Current residency and fellowship programs do not sufficiently prepare trainees to meet or exceed experience thresholds, so augmentation of training is necessary. A significant strength of VR simulation includes its ability to practice without limits. Unfortunately, hip models are limited to simple tasks, without full surgery models yet available simulating routine arthroscopic hip preservation procedures like labral repair, cam and pincer correction, capsular repair. Advanced techniques like labral reconstruction or augmentation, protrusio acetabulae, extensive cam morphology, revision surgery, peritrochanteric space endoscopy, and deep gluteal space endoscopy are not yet available for simulation. VR simulation can probably achieve competence for most, if not all, surgeons; possibly achieve proficiency; and unlikely to achieve mastery. The use of machine learning and artificial intelligence can process vast quantities of photo and video data to generate high-fidelity, lifelike surgical simulation. The near future will incorporate and assimilate these technologies cost-effectively for training programs and surgeons. Our patients will benefit.
Machine learning and artificial intelligence are increasingly used in modern health care, including arthroscopic and related surgery. Multiple high-quality, Level I evidence, randomized, controlled investigations have recently shown the ability of hip arthroscopy to successfully treat femoroacetabular impingement syndrome and labral tears. Contemporary hip preservation practice strives to continually refine and improve the value of care provision. Multiple single-center and multicenter prospective registries continue to grow as part of both United States-based and international hip preservation-specific networks and collaborations. The ability to predict postoperative patient-reported outcomes preoperatively holds great promise with machine learning. Machine learning requires massive amounts of data, which can easily be generated from electronic medical records and both patient- and clinician-generated questionnaires. On top of text-based data, imaging (e.g., plain radiographs, computed tomography, and magnetic resonance imaging) can be rapidly interpreted and used in both clinical practice and research. Formidable computational power is also required, using different advanced statistical methods and algorithms to generate models with the ability to predict individual patient outcomes. Efficient integration of machine learning into hip arthroscopy practice can reduce physicians' "busywork" of data collection and analysis. This can only improve the value of the patient experience, because surgeons have more time for shared decision making, with empathy, compassion, and humanity counterintuitively returning to medicine.
The use of advanced statistical methods and artificial intelligence including machine learning enables researchers to identify preoperative characteristics predictive of patients achieving minimal clinically important differences in health outcomes after interventions including surgery. Machine learning uses algorithms to recognize patterns in data sets to predict outcomes. The advantages are the ability, using “big data” registries, to infer relations that otherwise would not be readily understood and the ability to continuously improve the model as new data are added. However, machine learning has limitations. Models are only as good as the data incorporated, and data may be misapplied owing to huge data sets and strong computing capabilities, in which spurious correlations may be suggested based on significant P values. Hence, common sense must be applied. The future of outcome prediction studies will most definitely rely on machine learning and artificial intelligence methods.
Artificial intelligence (AI), including machine learning (ML), has transformed numerous industries through newfound efficiencies and supportive decision-making. With the exponential growth of computing power and large datasets, AI has transitioned from theory to reality in teaching machines to automate tasks without human supervision. AI-based computational algorithms analyze “training sets” using pattern recognition and learning from inputted data to classify and predict outputs that otherwise could not be effectively analyzed with human processing or standard statistical methods. Though widespread understanding of the fundamental principles and adoption of applications have yet to be achieved in orthopaedics, recent applications and research efforts implementing AI in the field of sports medicine have demonstrated great promise in predicting future athlete injury risk , interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting telehealth. With appreciation, caution, and experience applying AI to sports medicine, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. The purpose of this review is to discuss the pearls, pitfalls, and applications associated with AI as it relates to orthopaedic sports medicine.
Our journal has grown in pages including more articles plus commentary. On the one hand, we see this as a subscriber benefit, but we also recognize that more is not always better. We risk information overload resulting in fatigue and the inability to read every word of every article. The challenge of information overload has expanded since the explosion of the internet and electronic communications. We could increase our already high rejection rates, but at the risk of rejecting high-quality research, which we do not prefer. In the end, guided by our Journal Board of Trustees and the Arthroscopy Association of North America Board of Directors, our Editors and Associate Editors will continue to grapple with the positive challenge of robust growth.
Systematic reviews seem overly prevalent and often inconclusive. Yet, well-performed reviews provide powerful answers to clinical questions, whereas the results of a single clinical trial may not be reliably reproducible. Thus, in balance, we rest highly in favor of rigorously performed synthetic studies that stand at the top of the evidence-based medicine hierarchy.
Systematic Reviews (SRs) are becoming an increasingly utilized resource for readers that aims to answer a specific question by critically analyzing multiple research studies or papers on a topic. Although an SR can be extremely helpful to find an answer to a question, it may also be scrutinized, as the methodology is often not robust enough to adequately determine the outcome. This editorial serves to highlight the benefits of an SR, the methodology of a high-caliber SR, and some common pitfalls that may reduce the impact of an SR.
Sometimes systematic reviews seem overprevalent, and some systematic reviews can be “inconclusive,” which does not improve clinical decision making. On the other hand, systematic reviews can make a positive impact on patient outcomes by summarizing clinically relevant literature for arthroscopic surgeons and related researchers.
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.
The future of integrated electronics is the future of electronics itself. Integrated circuits will lead to such wonders as home computers, automatic controls for automobiles, and personal portable communications equipment. But the biggest potential lies in the production of large systems. In telephone communications, integrated circuits in digital filters will separate channels on multiplex equipment. Integrated circuits will also switch telephone circuits and perform data processing. In addition, the improved reliability made possible by integrated circuits will allow the construction of larger processing units. Machines similar to those in existence today will be built at lower costs and with faster turnaround.
An Artificial Intelligence Chatbot, Is Impacting Medical Literature. ArthroscopyVol. 39Issue 5p1121-1122Published online
- J Lubowitz
- Chatgpt
The utility of ChatGPT as an example of large language models in healthcare education, research and practice: Systematic review on the future perspectives and potential limitations
- Sallam
Hallucination, Fake References: Cautionary Tale About AI-Generated Abstracts
- Charles Burbank