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Graphic representation of an artificial neural network. Modeled after biological neural networks, artificial neural networks use input nodes, representing data input into the model; hidden nodes, responsible for making the predictions); and output nodes, representative of the predictions being made (Oncologists partner with Watson on genomics. Cancer Discov. 2015;5:788). During training, artificial neural networks, in a fashion similar to biological neurons, take part in a process called back-propagation, whereby the weight of the connections between nodes is adjusted based on the difference between the artificial neural networks output values and known target values. This process ensures that the output of the artificial neural network is as close as possible to the desired target values. (adapted from Meyfroidt G, Güiza F, Ramon J, Bruynooghe M. Machine learning techniques to examine large patient databases. Best Pract Res Clin Anaesthesiol. 2009;23:127-143.)
Source publication
Medical decision-making is increasingly based on quantifiable data. From the moment patients come into contact with the health care system, their entire medical history is recorded electronically. Whether a patient is in the operating room or on the hospital ward, technological advancement has facilitated the expedient and reliable measurement of c...
Context in source publication
Context 1
... to be used for prediction, intermediate or "hidden nodes" that calculate predictions based on the inputs, and output nodes that represent the predictions themselves. 6 During training, arti- ficial neural networks are tuned through a pro- cess of "back-propagation" where the accuracy of the output values is compared to the actual target values (Fig. 3). 6 In this investigation, normalized spectral data served as input nodes, whereas the two output nodes distinguished between spec- tra associated with burns that healed in less than 14 days and those associated with burns that took more than 14 days to heal. 11 After examining reflectance spectrometry data from 41 wounds, the ...
Citations
... There is promising data from early artificial intelligence studies that show high accuracy in predicting post-operative outcomes in plastic surgery/esthetic literature [57]. Though premature at the time of this review, advances in bioengineering would enable the precise replication of native tissue architecture, ensuring optimal compatibility and functionality. ...
Purpose of Review
Over the past decade, firearm-related injuries and fatalities have become more prevalent with head and neck injuries the most common in suicide-related attempts. This is accompanied by significant mortality and morbidity with devastating burden on healthcare systems. This review is a comprehensive examination of facial gunshot injuries over the past decade, integrating epidemiological trends, biomechanical insights, historical and contemporary reconstructive methods, and a forward-looking exploration of the future trajectory in managing such injuries.
Recent Findings
Firearm-related injuries and hospital admissions are demanding to the healthcare system with the total cost of 293,000 for a single patient [3, 4].
... Doctors often lack time and energy to update their knowledge while managing their busy clinical work. With the accumulation of large amounts of data and the establishment of databases, data analysis has brought the research of basic science to a whole new level, which makes the visualizing and preservation of experiential knowledge possible [9]. At the same time, the relevant clinical data and conclusions can also help AI learning [10]. ...
The growing discussion on "interdisciplinary integration" brings attention to the "interprofessional education" (IPE) in the field of plastic surgery. IPE not only improves the precision and effectiveness of plastic and reconstructive surgery but also plays an important role in personalized treatment. Whereas, the implementation of IPE in plastic and reconstructive surgery field faces huge difficulties such as technology combination, standard making, and lacking of qualified talents. This article individually summarizes the latest developments in the integration of plastic and reconstructive surgery with engineering, basic science, and human science. It looks forward to the future practice and innovation of IPE in the field of plastic and reconstructive surgery, analyzes the challenges in cultivating innovative professional talents, and proposes methods to overcome these difficulties in a way that invites further discussion.
... The role of ML and AI in craniofacial surgery has previously been thoroughly reviewed [4][5][6][7][8][9] . In a scoping review by Mak et al. (2020), the authors identified numerous craniofacial-based studies developing ML models [4] . ...
The volume and complexity of clinical data are growing rapidly. The potential for artificial intelligence (AI) and machine learning (ML) to significantly impact plastic and craniofacial surgery is immense. This manuscript reviews the overall landscape of AI in craniofacial surgery, highlighting the scarcity of prospective and clinically translated models. It examines the numerous clinical promises and challenges associated with AI, such as the lack of robust legislation and structured frameworks for its integration into medicine. Clinical translation considerations are discussed, including the importance of ensuring clinical utility for real-world use. Finally, this commentary brings forward how clinicians can build trust and sustainability toward model-driven clinical care.
... For instance, data-driven insights can help refine wound care practices, optimize pain management protocols, and reduce infection incidence. This continuous feedback loop enhances patient care and promotes collaboration and shared responsibility among physicians and nurses [94,95]. ...
In recent years, machine learning (ML) has emerged as a transformative technology in healthcare, providing significant advancements in patient care and management. Burn care, which necessitates comprehensive and coordinated efforts due to the severe and multifaceted nature of burn injuries, particularly benefits from ML's capabilities. This literature review investigates how ML enhances the collaboration between physicians and nurses in managing burn patients. In the present study, significant findings show that ML's predictive analytics can predict patient outcomes and complications, helping with proactive care strategies. ML-driven decision support systems offer real-time, evidence-based recommendations, ensuring consistent care approaches. In addition, ML-powered virtual simulations improve training and comprehension of roles, while advanced electronic health records (EHR) systems streamline documentation and information sharing. Continuous quality improvement is supported by ML's data-driven insights, leading to improved patient monitoring and management. Ultimately, integrating ML in burn care significantly improves physician-nurse collaboration, resulting in better patient outcomes. This includes reduced infection rates and shorter hospital stays. This highlights the vital role of ML in transforming healthcare delivery and professional collaboration in managing complex conditions like burn injuries.
... Possibilities of future implications of image analysis include, but are not limited to, the suggestion of different surgical techniques, detailed procedural descriptions, and enhanced prediction of patient outcomes. 11 ChatGPT may serve as a versatile tool in the realm of education. For students preparing for examinations, ChatGPT may act as an effective study aid, breaking down complex topics into more understandable content and providing insights into specific questions that would otherwise require timely research. ...
Background
ChatGPT, launched in 2022 and updated to Generative Pre-trained Transformer 4 (GPT-4) in 2023, is a large language model trained on extensive data, including medical information. This study compares ChatGPT’s performance on Plastic Surgery In-Service Examinations with medical residents nationally as well as its earlier version, ChatGPT-3.5.
Methods
This study reviewed 1500 questions from the Plastic Surgery In-service Examinations from 2018 to 2023. After excluding image-based, unscored, and inconclusive questions, 1292 were analyzed. The question stem and each multiple-choice answer was inputted verbatim into ChatGPT-4.
Results
ChatGPT-4 correctly answered 961 (74.4%) of the included questions. Best performance by section was in core surgical principles (79.1% correct) and lowest in craniomaxillofacial (69.1%). ChatGPT-4 ranked between the 61st and 97th percentiles compared with all residents. Comparatively, ChatGPT-4 significantly outperformed ChatGPT-3.5 in 2018–2022 examinations ( P < 0.001). Although ChatGPT-3.5 averaged 55.5% correctness, ChatGPT-4 averaged 74%, a mean difference of 18.54%. In 2021, ChatGPT-3.5 ranked in the 23rd percentile of all residents, whereas ChatGPT-4 ranked in the 97th percentile. ChatGPT-4 outperformed 80.7% of residents on average and scored above the 97th percentile among first-year residents. Its performance was comparable with sixth-year integrated residents, ranking in the 55.7th percentile, on average. These results show significant improvements in ChatGPT-4’s application of medical knowledge within six months of ChatGPT-3.5’s release.
Conclusion
This study reveals ChatGPT-4’s rapid developments, advancing from a first-year medical resident’s level to surpassing independent residents and matching a sixth-year resident’s proficiency.
... AI is becoming increasingly valuable in plastic surgery, especially for tasks requiring visual diagnosis, such as assessing preoperative and postoperative aesthetics [11] . Similarly, ML algorithms have also been used to improve outcome assessments in various procedures, such as rhinoplasty [12,13] . Additionally, facial recognition tools, a subtype of supervised learning in ML, have the potential to demonstrate the projected results of aesthetic surgeries, thereby assisting in managing patient expectations [2,14] . ...
... Few systematic reviews have explored the role of AI in plastic surgery in recent years [2,3,11,13] . While these reviews provide medical professionals with valuable insights into the emerging applications of AI across various fields of plastic surgery, they also highlight the need for further research in certain areas. ...
... Notably, Mantelakis et al. noted a significant gap in AI research related to hand surgery and the use of ML in this domain [3] . In addition, three [2,3,11] of the four systematic reviews were limited to articles published up to 2020, and the fourth [13] covered articles up to 2021. Given the rapidly increasing literature on the applications of AI in medicine, the aim of this systematic review was to provide a comprehensive analysis of its application within the field of hand and wrist surgery. ...
Artificial intelligence (AI) is currently utilized across numerous medical disciplines. Nevertheless, despite its promising advancements, AI’s integration in hand surgery remains in its early stages and has not yet been widely implemented, necessitating continued research to validate its efficacy and ensure its safety. Therefore, this review aims to provide an overview of the utilization of AI in hand surgery, emphasizing its current application in clinical practice, along with its potential benefits and associated challenges. A comprehensive literature search was conducted across PubMed, Embase, Medline, and Cochrane libraries, adhering to the Preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The search focused on identifying articles related to the application of AI in hand surgery, utilizing multiple relevant keywords. Each identified article was assessed based on its title, abstract, and full text. The primary search identified 1,228 articles; after the application of inclusion/exclusion criteria and manual bibliography search of included articles, a total of 98 articles were covered in this review. AI’s primary application in hand and wrist surgery is diagnostic, which includes hand and wrist fracture detection, carpal tunnel syndrome (CTS), avascular necrosis (AVN), and osteoporosis screening. Other applications include residents’ training, patient-doctor communication, surgical assistance, and outcome prediction. Consequently, AI is a very promising tool that has numerous applications in hand and wrist surgery, though further research is necessary to fully integrate it into clinical practice.
... In cardiology, for example, supervised learning has been tested for the purposes of risk calculation in cardiovascular disease, prediction of in-stent restenosis from plasma metabolites, and construction of a predictive model for acute myocardial infarction by way of proteomic measurements and clinical variables. [37][38][39] More broadly applicable surgical workflow applications of ML have been explored by the Computational Analysis and Modeling of Medical Activities groups at the University of Strasbourg; however, it is uncertain as to when these measures could be feasibly used in operating rooms around the world. 40 With many exciting developments on the rise, it is unsurprising that ML and AI have become an increasingly popular topic in plastic surgery. ...
Background
Machine learning (ML) is a form of artificial intelligence that has been used to create better predictive models in medicine. Using ML algorithms, we sought to create a predictive model for breast resection weight based on anthropometric measurements.
Methods
We analyzed 237 patients (474 individual breasts) who underwent reduction mammoplasty at our institution. Anthropometric variables included body surface area (BSA), body mass index, sternal notch–to–nipple (SN-N), and nipple–to–inframammary fold values. Four different ML algorithms (linear regression, ridge regression, support vector regression, and random forest regression) either including or excluding the Schnur Scale prediction for the same data were trained and tested on their ability to recognize the relationship between the anthropometric variables and total resection weights. Resection weight prediction accuracy for each model and the Schnur scale alone were evaluated based on using mean absolute error (MAE).
Results
In our cohort, mean age was 40.36 years. Most patients (71.61%) were African American. Mean BSA was 2.0 m ² , mean body mass index was 33.045 kg/m ² , mean SN-N was 35.0 cm, and mean nipple–to–inframammary fold was 16.0 cm. Mean SN-N was found to have the greatest variable importance. All 4 models made resection weight predictions with MAE lower than that of the Schnur Scale alone in both the training and testing datasets. Overall, the random forest regression model without Schnur scale weight had the lowest MAE at 186.20.
Conclusion
Our ML resection weight prediction model represents an accurate and promising alternative to the Schnur Scale in the setting of reduction mammaplasty consultations.
... From the mathematical basis of Artificial Intelligence to its commercially viable applications, topics introduced herein constitute a framework for design and execution of quantitative studies that will better outcomes and benefit patients. Finally, adherence to the principles of quality data collection will leverage and amplify plastic surgeons' creativity and undoubtedly drive the field forward [1]. ...
Using Artificial Intelligence Facial Plastic surgery is a new important tool that has been implemented recently into helping surgeons, we focused in two aspect of AI uses in facial plastics which is in the use of early detection of skin lesions and the use of it in the aspect of cosmetic facial plastic surgery by developing an algorithm that determines attractive facial features most closely related to post-operative target variables.
... Decision-making in cancer treatments is complex as it involves a diversity of data that need to be considered (131). Moreover, with the advances in medicine, new therapeutic options are proposed. ...
Background and Objective
We have witnessed tremendous advances in artificial intelligence (AI) technologies. Breast surgery, a subspecialty of general surgery, has notably benefited from AI technologies. This review aims to evaluate how AI has been integrated into breast surgery practices, to assess its effectiveness in improving surgical outcomes and operational efficiency, and to identify potential areas for future research and application.
Methods
Two authors independently conducted a comprehensive search of PubMed, Google Scholar, EMBASE, and Cochrane CENTRAL databases from January 1, 1950, to September 4, 2023, employing keywords pertinent to AI in conjunction with breast surgery or cancer. The search focused on English language publications, where relevance was determined through meticulous screening of titles, abstracts, and full-texts, followed by an additional review of references within these articles. The review covered a range of studies illustrating the applications of AI in breast surgery encompassing lesion diagnosis to postoperative follow-up. Publications focusing specifically on breast reconstruction were excluded.
Key Content and Findings
AI models have preoperative, intraoperative, and postoperative applications in the field of breast surgery. Using breast imaging scans and patient data, AI models have been designed to predict the risk of breast cancer and determine the need for breast cancer surgery. In addition, using breast imaging scans and histopathological slides, models were used for detecting, classifying, segmenting, grading, and staging breast tumors. Preoperative applications included patient education and the display of expected aesthetic outcomes. Models were also designed to provide intraoperative assistance for precise tumor resection and margin status assessment. As well, AI was used to predict postoperative complications, survival, and cancer recurrence.
Conclusions
Extra research is required to move AI models from the experimental stage to actual implementation in healthcare. With the rapid evolution of AI, further applications are expected in the coming years including direct performance of breast surgery. Breast surgeons should be updated with the advances in AI applications in breast surgery to provide the best care for their patients.
... Este modelo alcanzó una precisión predictiva de 86%, sugiriendo que puede ser una herramienta efectiva para la evaluación de las quemaduras y una alternativa superior a la evaluación visual directa realizada por los cirujanos plásticos. 18 Herramientas convencionales como la «regla de los nueve», presentan limitaciones debido a la asimetría de las lesiones, las variaciones en el área de superficie debido a la edad del paciente y la variabilidad interobservador. Se han desarrollado herramientas basadas en IA para medir con precisión la superficie lesionada, logrando altos porcentajes de precisión, en comparación con la evaluación visual realizada por los médicos, ofreciendo un aporte hídrico más preciso. ...
... Estos resultados resaltan el potencial de la IA en el análisis y desarrollo de estrategias de ingeniería tisular para la reparación de nervios periféricos. 18 Daeschler y su grupo desarrollaron un modelo de IA que permite la segmentación y morfometría automatizada de fibras nerviosas periféricas a partir de imágenes microscópicas de luz. Aunque los avances se han obtenido en estudios realizados en ratones, esta herramienta tiene un alto potencial en la regeneración nerviosa y podría beneficiar a pacientes con neuropatías. ...
La medicina basada en la evidencia se enfoca en brindar
atención apoyada en información validada, objetiva y de
alta calidad. En los últimos tiempos, la aparición de la
inteligencia artificial ha transformado la práctica clínica.
Esta tecnología equipa a los profesionales médicos con
un sólido conjunto de herramientas para examinar vastos
conjuntos de datos y administrar medicamentos basados en
la mejor evidencia. El campo de la cirugía plástica no ha
tardado en acoger esta ola tecnológica, integrando progresivamente herramientas de inteligencia artificial en diversas
facetas como el diagnóstico, la evaluación del tratamiento
y la formación de los futuros cirujanos. Este trabajo tiene
como objetivo proporcionar una revisión completa de la
literatura que delinee de manera integral las aplicaciones
existentes de la inteligencia artificial dentro de nuestro campo especializado. El potencial de la inteligencia artificial
para revolucionar la cirugía plástica es rotundo, promete
mejorar la precisión, eficacia y seguridad de los procedimientos quirúrgicos. Además, se destaca en la evaluación
de los resultados y cataliza la formulación de alternativas
terapéuticas. Este trabajo subraya el papel fundamental que
la inteligencia artificial está a punto de desempeñar en la
evolución de la cirugía plástica, transformándola en una
disciplina más refinada y con visión de futuro.
