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Adegboye FO, Peterson AC, Sharma RK, Stephan SJ, Patel PN, Yang SF. Applications of Artificial Intelligence in Facial Plastic and Reconstructive Surgery: A Narrative Review. Facial Plast Surg Aesthet Med 2024. [PMID: 39413311 DOI: 10.1089/fpsam.2024.0129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2024] Open
Abstract
Importance: Artificial intelligence (AI) has made invaluable contributions to the technologic advancements across many fields. It is transforming health care and may have a role in improving patient outcomes in facial plastic and reconstructive surgery (FPRS). Observations: In recent years, new automated approaches to simulating and analyzing outcomes using AI have emerged. Advances in rhinoplasty, facelifts, orthognathic surgery, facial reanimation, and preoperative consultation are currently being developed in FPRS. Conclusions and Relevance: Applications of AI have been applied to assist facial plastic surgeons in the preoperative stage, intraoperative planning process, and objective assessment of postoperative outcomes. The application of AI provides avenues to improve postoperative outcomes, while also optimizing patient care.
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Affiliation(s)
- Feyisayo O Adegboye
- Department of Otolaryngology-Head and Neck surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - April C Peterson
- Department of Otolaryngology-Head and Neck surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Rahul K Sharma
- Department of Otolaryngology-Head and Neck surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Scott J Stephan
- Department of Otolaryngology-Head and Neck surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Priyesh N Patel
- Department of Otolaryngology-Head and Neck surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Shiayin F Yang
- Department of Otolaryngology-Head and Neck surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Lechien JR, Rameau A. Applications of ChatGPT in Otolaryngology-Head Neck Surgery: A State of the Art Review. Otolaryngol Head Neck Surg 2024; 171:667-677. [PMID: 38716790 DOI: 10.1002/ohn.807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/01/2024] [Accepted: 04/19/2024] [Indexed: 08/28/2024]
Abstract
OBJECTIVE To review the current literature on the application, accuracy, and performance of Chatbot Generative Pre-Trained Transformer (ChatGPT) in Otolaryngology-Head and Neck Surgery. DATA SOURCES PubMED, Cochrane Library, and Scopus. REVIEW METHODS A comprehensive review of the literature on the applications of ChatGPT in otolaryngology was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. CONCLUSIONS ChatGPT provides imperfect patient information or general knowledge related to diseases found in Otolaryngology-Head and Neck Surgery. In clinical practice, despite suboptimal performance, studies reported that the model is more accurate in providing diagnoses, than in suggesting the most adequate additional examinations and treatments related to clinical vignettes or real clinical cases. ChatGPT has been used as an adjunct tool to improve scientific reports (referencing, spelling correction), to elaborate study protocols, or to take student or resident exams reporting several levels of accuracy. The stability of ChatGPT responses throughout repeated questions appeared high but many studies reported some hallucination events, particularly in providing scientific references. IMPLICATIONS FOR PRACTICE To date, most applications of ChatGPT are limited in generating disease or treatment information, and in the improvement of the management of clinical cases. The lack of comparison of ChatGPT performance with other large language models is the main limitation of the current research. Its ability to analyze clinical images has not yet been investigated in otolaryngology although upper airway tract or ear images are an important step in the diagnosis of most common ear, nose, and throat conditions. This review may help otolaryngologists to conceive new applications in further research.
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Affiliation(s)
- Jérôme R Lechien
- Research Committee of Young Otolaryngologists of the International Federation of Otorhinolaryngological Societies (IFOS), Paris, France
- Division of Laryngology and Broncho-Esophagology, Department of Otolaryngology-Head Neck Surgery, EpiCURA Hospital, UMONS Research Institute for Health Sciences and Technology, University of Mons (UMons), Mons, Belgium
- Department of Otorhinolaryngology and Head and Neck Surgery, Foch Hospital, Phonetics and Phonology Laboratory (UMR 7018 CNRS, Université Sorbonne Nouvelle/Paris 3), Paris Saclay University, Paris, France
- Department of Otorhinolaryngology and Head and Neck Surgery, CHU Saint-Pierre, Brussels, Belgium
| | - Anais Rameau
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medicine, New York City, New York, USA
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Dhawan R, Shay D. Artificial intelligence in plastic surgery: Implications and limitations of text-to-image models for clinical practice. JPRAS Open 2024; 41:368-371. [PMID: 39224909 PMCID: PMC11367565 DOI: 10.1016/j.jpra.2024.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Accepted: 07/25/2024] [Indexed: 09/04/2024] Open
Affiliation(s)
- Ravi Dhawan
- Emory University Department of Plastic Surgery, 101 Woodruff Circle, Atlanta, GA, USA
| | - Denys Shay
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, USA
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Haider SA, Pressman SM, Borna S, Gomez-Cabello CA, Sehgal A, Leibovich BC, Forte AJ. Evaluating Large Language Model (LLM) Performance on Established Breast Classification Systems. Diagnostics (Basel) 2024; 14:1491. [PMID: 39061628 PMCID: PMC11275570 DOI: 10.3390/diagnostics14141491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 06/25/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
Medical researchers are increasingly utilizing advanced LLMs like ChatGPT-4 and Gemini to enhance diagnostic processes in the medical field. This research focuses on their ability to comprehend and apply complex medical classification systems for breast conditions, which can significantly aid plastic surgeons in making informed decisions for diagnosis and treatment, ultimately leading to improved patient outcomes. Fifty clinical scenarios were created to evaluate the classification accuracy of each LLM across five established breast-related classification systems. Scores from 0 to 2 were assigned to LLM responses to denote incorrect, partially correct, or completely correct classifications. Descriptive statistics were employed to compare the performances of ChatGPT-4 and Gemini. Gemini exhibited superior overall performance, achieving 98% accuracy compared to ChatGPT-4's 71%. While both models performed well in the Baker classification for capsular contracture and UTSW classification for gynecomastia, Gemini consistently outperformed ChatGPT-4 in other systems, such as the Fischer Grade Classification for gender-affirming mastectomy, Kajava Classification for ectopic breast tissue, and Regnault Classification for breast ptosis. With further development, integrating LLMs into plastic surgery practice will likely enhance diagnostic support and decision making.
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Affiliation(s)
- Syed Ali Haider
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - Sahar Borna
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - Ajai Sehgal
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
| | - Bradley C. Leibovich
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
- Department of Urology, Mayo Clinic, Rochester, MN 55905, USA
| | - Antonio Jorge Forte
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
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Atkinson CJ, Seth I, Seifman MA, Rozen WM, Cuomo R. Enhancing Hand Fracture Care: A Prospective Study of Artificial Intelligence Application With ChatGPT. JOURNAL OF HAND SURGERY GLOBAL ONLINE 2024; 6:524-528. [PMID: 39166196 PMCID: PMC11331228 DOI: 10.1016/j.jhsg.2024.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 03/26/2024] [Indexed: 08/22/2024] Open
Abstract
Purpose The integration of artificial intelligence and machine learning technologies into the medical field has brought about remarkable advancements, particularly in the domain of clinical decision support systems. However, it is uncertain how they will perform as clinical decision-makers. Methods This prospective cohort study evaluates the potential of incorporating ChatGPT-4 plus into the management of subcapital fifth metacarpal fractures. The treatment recommendations provided by ChatGPT-4 plus were compared with those of the two control groups-the attending clinic plastic surgeon and an independent expert panel. The primary outcome measures, operative or conservative, were compared between the groups. Intraclass correlation of 0.61 infers moderate reliability in the consistency of recommended management plans across all groups. Results Key predictors for opting for operative management, regardless of the decision-maker, included clinical signs of scissoring, extension deficit, and radiographic evidence of intra-articular extension. Conclusions These findings support the potential for artificial intelligence applications in enhancing diagnostic and treatment decisions. Type of study/level of evidence Therapeutic IV.
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Affiliation(s)
- Connor John Atkinson
- Department of Plastic and Reconstructive Surgery, Frankston Hospital, Peninsula Health, Frankston, VIC, Australia
| | - Ishith Seth
- Department of Plastic and Reconstructive Surgery, Frankston Hospital, Peninsula Health, Frankston, VIC, Australia
- Department of Surgery, Central Clinical School, Monash University, Alfred Hospital, Prahran, VIC, Australia
| | - Marc Adam Seifman
- Department of Plastic and Reconstructive Surgery, Frankston Hospital, Peninsula Health, Frankston, VIC, Australia
| | - Warren Matthew Rozen
- Department of Plastic and Reconstructive Surgery, Frankston Hospital, Peninsula Health, Frankston, VIC, Australia
- Department of Surgery, Central Clinical School, Monash University, Alfred Hospital, Prahran, VIC, Australia
| | - Roberto Cuomo
- Plastic Surgery Unit, Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
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Lim B, Seth I, Cuomo R, Kenney PS, Ross RJ, Sofiadellis F, Pentangelo P, Ceccaroni A, Alfano C, Rozen WM. Can AI Answer My Questions? Utilizing Artificial Intelligence in the Perioperative Assessment for Abdominoplasty Patients. Aesthetic Plast Surg 2024:10.1007/s00266-024-04157-0. [PMID: 38898239 DOI: 10.1007/s00266-024-04157-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024]
Abstract
BACKGROUND Abdominoplasty is a common operation, used for a range of cosmetic and functional issues, often in the context of divarication of recti, significant weight loss, and after pregnancy. Despite this, patient-surgeon communication gaps can hinder informed decision-making. The integration of large language models (LLMs) in healthcare offers potential for enhancing patient information. This study evaluated the feasibility of using LLMs for answering perioperative queries. METHODS This study assessed the efficacy of four leading LLMs-OpenAI's ChatGPT-3.5, Anthropic's Claude, Google's Gemini, and Bing's CoPilot-using fifteen unique prompts. All outputs were evaluated using the Flesch-Kincaid, Flesch Reading Ease score, and Coleman-Liau index for readability assessment. The DISCERN score and a Likert scale were utilized to evaluate quality. Scores were assigned by two plastic surgical residents and then reviewed and discussed until a consensus was reached by five plastic surgeon specialists. RESULTS ChatGPT-3.5 required the highest level for comprehension, followed by Gemini, Claude, then CoPilot. Claude provided the most appropriate and actionable advice. In terms of patient-friendliness, CoPilot outperformed the rest, enhancing engagement and information comprehensiveness. ChatGPT-3.5 and Gemini offered adequate, though unremarkable, advice, employing more professional language. CoPilot uniquely included visual aids and was the only model to use hyperlinks, although they were not very helpful and acceptable, and it faced limitations in responding to certain queries. CONCLUSION ChatGPT-3.5, Gemini, Claude, and Bing's CoPilot showcased differences in readability and reliability. LLMs offer unique advantages for patient care but require careful selection. Future research should integrate LLM strengths and address weaknesses for optimal patient education. LEVEL OF EVIDENCE V 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 .
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Affiliation(s)
- Bryan Lim
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
| | - Ishith Seth
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
| | - Roberto Cuomo
- Plastic Surgery Unit, Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
| | - Peter Sinkjær Kenney
- Department of Plastic Surgery, Velje Hospital, Beriderbakken 4, 7100, Vejle, Denmark
- Department of Plastic and Breast Surgery, Aarhus University Hospital, Aarhus, Denmark
| | - Richard J Ross
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
| | - Foti Sofiadellis
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
| | | | | | | | - Warren Matthew Rozen
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
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Lim B, Cevik J, Seth I, Sofiadellis F, Ross RJ, Rozen WM, Cuomo R. Evaluating Artificial Intelligence's Role in Teaching the Reporting and Interpretation of Computed Tomographic Angiography for Preoperative Planning of the Deep Inferior Epigastric Artery Perforator Flap. JPRAS Open 2024; 40:273-285. [PMID: 38708385 PMCID: PMC11067004 DOI: 10.1016/j.jpra.2024.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 03/30/2024] [Indexed: 05/07/2024] Open
Abstract
Background Artificial intelligence (AI) has the potential to transform preoperative planning for breast reconstruction by enhancing the efficiency, accuracy, and reliability of radiology reporting through automatic interpretation and perforator identification. Large language models (LLMs) have recently advanced significantly in medicine. This study aimed to evaluate the proficiency of contemporary LLMs in interpreting computed tomography angiography (CTA) scans for deep inferior epigastric perforator (DIEP) flap preoperative planning. Methods Four prominent LLMs, ChatGPT-4, BARD, Perplexity, and BingAI, answered six questions on CTA scan reporting. A panel of expert plastic surgeons with extensive experience in breast reconstruction assessed the responses using a Likert scale. In contrast, the responses' readability was evaluated using the Flesch Reading Ease score, the Flesch-Kincaid Grade level, and the Coleman-Liau Index. The DISCERN score was utilized to determine the responses' suitability. Statistical significance was identified through a t-test, and P-values < 0.05 were considered significant. Results BingAI provided the most accurate and useful responses to prompts, followed by Perplexity, ChatGPT, and then BARD. BingAI had the greatest Flesh Reading Ease (34.7±5.5) and DISCERN (60.5±3.9) scores. Perplexity had higher Flesch-Kincaid Grade level (20.5±2.7) and Coleman-Liau Index (17.8±1.6) scores than other LLMs. Conclusion LLMs exhibit limitations in their capabilities of reporting CTA for preoperative planning of breast reconstruction, yet the rapid advancements in technology hint at a promising future. AI stands poised to enhance the education of CTA reporting and aid preoperative planning. In the future, AI technology could provide automatic CTA interpretation, enhancing the efficiency, accuracy, and reliability of CTA reports.
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Affiliation(s)
- Bryan Lim
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
- Faculty of Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Jevan Cevik
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
- Faculty of Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Ishith Seth
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
- Faculty of Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Foti Sofiadellis
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
| | - Richard J. Ross
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
| | - Warren M. Rozen
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
- Faculty of Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Roberto Cuomo
- Plastic Surgery Unit, Department of Medicine, Surgery and Neuroscience, University of Siena, 53100, Italy
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Tan S, Xin X, Wu D. ChatGPT in medicine: prospects and challenges: a review article. Int J Surg 2024; 110:3701-3706. [PMID: 38502861 PMCID: PMC11175750 DOI: 10.1097/js9.0000000000001312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 02/26/2024] [Indexed: 03/21/2024]
Abstract
It has been a year since the launch of Chat Generator Pre-Trained Transformer (ChatGPT), a generative artificial intelligence (AI) program. The introduction of this cross-generational product initially brought a huge shock to people with its incredible potential and then aroused increasing concerns among people. In the field of medicine, researchers have extensively explored the possible applications of ChatGPT and achieved numerous satisfactory results. However, opportunities and issues always come together. Problems have also been exposed during the applications of ChatGPT, requiring cautious handling, thorough consideration, and further guidelines for safe use. Here, the authors summarized the potential applications of ChatGPT in the medical field, including revolutionizing healthcare consultation, assisting patient management and treatment, transforming medical education, and facilitating clinical research. Meanwhile, the authors also enumerated researchers' concerns arising along with its broad and satisfactory applications. As it is irreversible that AI will gradually permeate every aspect of modern life, the authors hope that this review can not only promote people's understanding of the potential applications of ChatGPT in the future but also remind them to be more cautious about this "Pandora's Box" in the medical field. It is necessary to establish normative guidelines for its safe use in the medical field as soon as possible.
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Affiliation(s)
| | | | - Di Wu
- Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shijingshan, Beijing, China
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Ciccarelli F, Pieretti G, Seth I. The Effect of Drains and Compressive Garments Versus Progressive Tensioning Sutures on Seroma Formation in Abdominoplasty: A New Perspective for Abdominoplasty Procedure? Aesthetic Plast Surg 2024:10.1007/s00266-023-03817-x. [PMID: 38191862 DOI: 10.1007/s00266-023-03817-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/13/2023] [Indexed: 01/10/2024]
Abstract
Authors comment on the paper "The effect of drains and compressive garments versus progressive tensioning sutures on seroma formation in abdominoplasty" written by Brown et al in Aesthetic Plastic Surgery.Although the authors present interesting results on the effectiveness of progressive tensioning sutures proposed originally by Pollok and Pollok, we express some considerations about the analyzed data and patients, hoping in a new research extending these findings to include both aesthetic and post-bariatric abdominoplasty patients, evaluating the effectiveness of these sutures in varied contexts.Level of Evidence V 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 Table of Contents or online Instructions to Authors www.springer.com/00266 .
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Affiliation(s)
| | - Gorizio Pieretti
- Plastic and Reconstructive Surgery Unit, Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Ishith Seth
- Department of Plastic and Reconstructive Surgery, Frankston Hospital, Peninsula Health, Frankston, VIC, Australia
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