{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T03:03:47Z","timestamp":1793675027825,"version":"4.5.1"},"reference-count":119,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T00:00:00Z","timestamp":1747699200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T00:00:00Z","timestamp":1747699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Educ"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) holds transformative potential for graduate medical education (GME), yet, a comprehensive exploration of AI\u2019s applications, perceptions, and limitations in GME is lacking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To map the current literature on AI in GME, identifying prevailing perceptions, applications, and research gaps to inform future research, policy discussions, and educational practices through a scoping review.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Following the Joanna Briggs Institute guidelines and the PRISMA-ScR checklist a comprehensive search of multiple databases up to February 2024 was performed to include studies addressing AI interventions in GME.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Out of 1734 citations, 102 studies met the inclusion criteria, conducted across 16 countries, predominantly from North America (72), Asia (14), and Europe (6). Radiology had the highest number of publications (21), followed by general surgery (11) and emergency medicine (8). The majority of studies were published in 2023. Several key thematic areas emerged from the literature. Initially, perceptions of AI in graduate medical education (GME) were mixed, but have increasingly shifted toward a more favorable outlook, particularly as the benefits of AI integration in education become more apparent. In assessments, AI demonstrated the ability to differentiate between skill levels and offer meaningful feedback. It has also been effective in evaluating narrative comments to assess resident performance. In the domain of recruitment, AI tools have been applied to analyze letters of recommendation, applications, and personal statements, helping identify potential biases and improve equity in candidate selection. Furthermore, large language models consistently outperformed average candidates on board certification and in-training examinations, indicating their potential utility in standardized assessments. Finally, AI tools showed promise in enhancing clinical decision-making by supporting trainees with improved diagnostic accuracy and efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This scoping review provides a comprehensive overview of applications and limitations of AI in GME but is limited with potential biases, study heterogeneity, and evolving nature of AI.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12909-025-07321-5","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T09:32:14Z","timestamp":1747733534000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Modern artificial intelligence and large language models in graduate medical education: a scoping review of attitudes, applications &amp; practice"],"prefix":"10.1186","volume":"25","author":[{"given":"Basil George","family":"Verghese","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charoo","family":"Iyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tanvi","family":"Borse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiamak","family":"Cooper","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"White","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan","family":"Sheehy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,20]]},"reference":[{"issue":"7553","key":"7321_CR1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436\u201344.","journal-title":"Nature"},{"key":"7321_CR2","volume-title":"Introduction to AI robotics","author":"R Murphy","year":"2018","unstructured":"Murphy R. Introduction to AI robotics. Cambridge, MA: The MIT Press; 2018."},{"issue":"7","key":"7321_CR3","doi-asserted-by":"publisher","first-page":"2328","DOI":"10.4103\/jfmpc.jfmpc_440_19","volume":"8","author":"P Amisha, Malik","year":"2019","unstructured":"Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Fam Med Prim Care. 2019;8(7):2328\u201331.","journal-title":"J Fam Med Prim Care"},{"issue":"1","key":"7321_CR4","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1111\/medu.14131","volume":"55","author":"AG Van Der Niet","year":"2021","unstructured":"Van Der Niet AG, Bleakley A. Where medical education Meets artificial intelligence: \u2018does technology care?\u2019. Med Educ. 2021;55(1):30\u20136.","journal-title":"Med Educ"},{"key":"7321_CR5","first-page":"648","volume":"305","author":"F Nagi","year":"2023","unstructured":"Nagi F, Salih R, Alzubaidi M, Shah H, Alam T, Shah Z, et al. Applications of artificial intelligence (AI) in medical education: A scoping review. Stud Health Technol Inf. 2023;305:648\u201351.","journal-title":"Stud Health Technol Inf"},{"issue":"4","key":"7321_CR6","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1513\/AnnalsATS.201402-068PS","volume":"11","author":"ME Maldonado","year":"2014","unstructured":"Maldonado ME, Fried ED, DuBose TD, Nelson C, Breida M. The role that graduate medical education must play in ensuring health equity and eliminating health care disparities. Ann Am Thorac Soc. 2014;11(4):603\u20137.","journal-title":"Ann Am Thorac Soc"},{"key":"7321_CR7","doi-asserted-by":"crossref","unstructured":"Stawicki P, Kumar S, Firstenberg KNS, Orlando MP, Papadimos JJ, Paul T et al. E, Introductory Chapter: Navigating Challenges and Opportunities in Modern Graduate Medical Education. In: P. Stawicki S, S. Firstenberg M, P. Orlando J, J. Papadimos T, editors. Contemporary Topics in Graduate Medical Education - Volume 2. IntechOpen; 2022 [cited 2024 Jan 8]. Available from: https:\/\/www.intechopen.com\/chapters\/79744","DOI":"10.5772\/intechopen.101016"},{"issue":"3","key":"7321_CR8","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1038\/s41587-022-01253-x","volume":"40","author":"O Boms","year":"2022","unstructured":"Boms O, Shi Z, Mallipeddi N, Chung JJ, Marks WH, Whitehead DC, et al. Integrating innovation as a core objective in medical training. Nat Biotechnol. 2022;40(3):434\u20137.","journal-title":"Nat Biotechnol"},{"key":"7321_CR9","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.clinimag.2022.09.003","volume":"92","author":"T Shiang","year":"2022","unstructured":"Shiang T, Garwood E, Debenedectis CM. Artificial intelligence-based decision support system (AI-DSS) implementation in radiology residency: introducing residents to AI in the clinical setting. Clin Imaging. 2022;92:32\u20137.","journal-title":"Clin Imaging"},{"issue":"4","key":"7321_CR10","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1097\/SIH.0000000000000373","volume":"14","author":"WF Bond","year":"2019","unstructured":"Bond WF, Lynch TJ, Mischler MJ, Fish JL, McGarvey JS, Taylor JT, et al. Virtual standardized patient simulation: case development and pilot application to High-Value care. Simul Healthc J Soc Simul Healthc. 2019;14(4):241\u201350.","journal-title":"Simul Healthc J Soc Simul Healthc"},{"key":"7321_CR11","doi-asserted-by":"crossref","unstructured":"Lee J, Wu AS, Li D, Kulasegaram K (Mahan), editors. Artificial Intelligence in Undergraduate Medical Education: A Scoping Review. Acad Med. 2021;96(11S):S62\u201370.","DOI":"10.1097\/ACM.0000000000004291"},{"issue":"2","key":"7321_CR12","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1016\/j.jsurg.2021.09.012","volume":"79","author":"A Kirubarajan","year":"2022","unstructured":"Kirubarajan A, Young D, Khan S, Crasto N, Sobel M, Sussman D. Artificial intelligence and surgical education: A systematic scoping review of interventions. J Surg Educ. 2022;79(2):500\u201315.","journal-title":"J Surg Educ"},{"key":"7321_CR13","doi-asserted-by":"crossref","unstructured":"Abdel Aziz MH, Rowe C, Southwood R, Nogid A, Berman S, Gustafson K. A scoping review of artificial intelligence within pharmacy education. Am J Pharm Educ. 2023;100615.","DOI":"10.1016\/j.ajpe.2023.100615"},{"issue":"1","key":"7321_CR14","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1080\/1364557032000119616","volume":"8","author":"H Arksey","year":"2005","unstructured":"Arksey H, O\u2019Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19\u201332.","journal-title":"Int J Soc Res Methodol"},{"issue":"3","key":"7321_CR15","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1097\/XEB.0000000000000050","volume":"13","author":"MDJ Peters","year":"2015","unstructured":"Peters MDJ, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc. 2015;13(3):141\u20136.","journal-title":"Int J Evid Based Healthc"},{"issue":"7","key":"7321_CR16","doi-asserted-by":"publisher","first-page":"467","DOI":"10.7326\/M18-0850","volume":"169","author":"AC Tricco","year":"2018","unstructured":"Tricco AC, Lillie E, Zarin W, O\u2019Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467\u201373.","journal-title":"Ann Intern Med"},{"key":"7321_CR17","unstructured":"Sheehy R, White J, Verghese B, Iyer C. Protocol for A Scoping Review of Artificial Intelligence in Graduate Medical Education. OSF Registries; 2024 [cited 2024 Jul 16]. Available from: https:\/\/osf.io\/uw2n7\/"},{"key":"7321_CR18","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.clinimag.2021.09.018","volume":"81","author":"K Reeder","year":"2022","unstructured":"Reeder K, Lee H. Impact of artificial intelligence on US medical students\u2019 choice of radiology. Clin Imaging. 2022;81:67\u201371.","journal-title":"Clin Imaging"},{"issue":"12","key":"7321_CR19","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1016\/j.jacr.2017.12.021","volume":"15","author":"F Collado-Mesa","year":"2018","unstructured":"Collado-Mesa F, Alvarez E, Arheart K. The role of artificial intelligence in diagnostic radiology: A survey at a single radiology residency training program. J Am Coll Radiol. 2018;15(12):1753\u20137.","journal-title":"J Am Coll Radiol"},{"issue":"3","key":"7321_CR20","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1177\/08465371211049553","volume":"73","author":"T Wu","year":"2022","unstructured":"Wu T, Law W, Islam N, Yong-Hing CJ, Kulkarni S, Seely J. Factors influencing trainees\u2019 interest in breast imaging. Can Assoc Radiol J. 2022;73(3):462\u201372.","journal-title":"Can Assoc Radiol J"},{"key":"7321_CR21","doi-asserted-by":"publisher","first-page":"S58","DOI":"10.1016\/S0167-8140(22)04416-4","volume":"174","author":"T Kennedy","year":"2022","unstructured":"Kennedy T, Collie L, Nabhen J, Safavi A, Brundage M, De Moraes FY. 136: Canadian oncology residents\u2019 knowledge of and attitudes towards artificial intelligence and machine learning. Radiother Oncol. 2022;174:S58\u20139.","journal-title":"Radiother Oncol"},{"issue":"9","key":"7321_CR22","doi-asserted-by":"publisher","first-page":"7058","DOI":"10.1007\/s00330-021-07781-5","volume":"31","author":"M Huisman","year":"2021","unstructured":"Huisman M, Ranschaert E, Parker W, Mastrodicasa D, Koci M, Pinto De Santos D, et al. An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude. Eur Radiol. 2021;31(9):7058\u201366.","journal-title":"Eur Radiol"},{"key":"7321_CR23","doi-asserted-by":"publisher","first-page":"e48249","DOI":"10.2196\/48249","volume":"25","author":"Y Chen","year":"2023","unstructured":"Chen Y, Wu Z, Wang P, Xie L, Yan M, Jiang M, et al. Radiology residents\u2019 perceptions of artificial intelligence: nationwide Cross-Sectional survey study. J Med Internet Res. 2023;25:e48249.","journal-title":"J Med Internet Res"},{"issue":"3","key":"7321_CR24","doi-asserted-by":"publisher","first-page":"126","DOI":"10.11622\/smedj.2019141","volume":"62","author":"S Ooi","year":"2021","unstructured":"Ooi S, Makmur A, Soon Y, Fook-Chong S, Liew C, Sia D, et al. Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a National multi-programme survey. Singap Med J. 2021;62(3):126\u201334.","journal-title":"Singap Med J"},{"key":"7321_CR25","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.clinimag.2023.02.008","volume":"98","author":"KM Marquis","year":"2023","unstructured":"Marquis KM, Hoegger MJ, Shetty AS, Bishop GL, Balthazar P, Gould JE, et al. Results of the 2020 survey of the American alliance of academic chief residents in radiology. Clin Imaging. 2023;98:67\u201373.","journal-title":"Clin Imaging"},{"issue":"7","key":"7321_CR26","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1016\/j.acra.2023.01.005","volume":"30","author":"NV Salastekar","year":"2023","unstructured":"Salastekar NV, Maxfield C, Hanna TN, Krupinski EA, Heitkamp D, Grimm LJ. Artificial intelligence\/machine learning education in radiology: Multi-institutional survey of radiology residents in the united States. Acad Radiol. 2023;30(7):1481\u20137.","journal-title":"Acad Radiol"},{"key":"7321_CR27","unstructured":"Kocer Tulgar Y, Department of Medical History and Ethics, Medicine SU, Turkey S, Tulgar S, Department of Anaesthesiology and Reanimation, Samsun University Faculty of Medicine, Samsun Training and Research Hospital, Samsun, Turkey, Kose G, Kose S et al. HC,. Anesthesiologists\u2019 Perspective on the Use of Artificial Intelligence in Ultrasound-Guided Regional Anaesthesia in Terms of Medical Ethics and Medical Education: A Survey Study. Eurasian J Med. 2023 May 5 [cited 2024 May 18]; Available from: https:\/\/www.eajm.org\/\/en\/anesthesiologists-perspective-on-the-use-of-artificial-intelligence-in-ultrasound-guided-regional-anaesthesia-in-terms-of-medical-ethics-and-medical-education-a-survey-study-133475"},{"issue":"1","key":"7321_CR28","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1038\/s41746-022-00596-8","volume":"5","author":"R Yilmaz","year":"2022","unstructured":"Yilmaz R, Winkler-Schwartz A, Mirchi N, Reich A, Christie S, Tran DH, et al. Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation. Npj Digit Med. 2022;5(1):54.","journal-title":"Npj Digit Med"},{"issue":"2","key":"7321_CR29","doi-asserted-by":"publisher","first-page":"63","DOI":"10.3109\/10929080801957712","volume":"13","author":"C Sewell","year":"2008","unstructured":"Sewell C, Morris D, Blevins NH, Dutta S, Agrawal S, Barbagli F, et al. Providing metrics and performance feedback in a surgical simulator. Comput Aided Surg. 2008;13(2):63\u201381.","journal-title":"Comput Aided Surg"},{"issue":"1","key":"7321_CR30","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1227\/ons.0000000000000173","volume":"23","author":"A Reich","year":"2022","unstructured":"Reich A, Mirchi N, Yilmaz R, Ledwos N, Bissonnette V, Tran DH, et al. Artificial neural network approach to Competency-Based training using a virtual reality neurosurgical simulation. Oper Neurosurg. 2022;23(1):31\u20139.","journal-title":"Oper Neurosurg"},{"key":"7321_CR31","doi-asserted-by":"publisher","first-page":"104770","DOI":"10.1016\/j.compbiomed.2021.104770","volume":"136","author":"S Alkadri","year":"2021","unstructured":"Alkadri S, Ledwos N, Mirchi N, Reich A, Yilmaz R, Driscoll M, et al. Utilizing a multilayer perceptron artificial neural network to assess a virtual reality surgical procedure. Comput Biol Med. 2021;136:104770.","journal-title":"Comput Biol Med"},{"issue":"6","key":"7321_CR32","doi-asserted-by":"publisher","first-page":"e263","DOI":"10.1016\/j.jsurg.2020.09.022","volume":"79","author":"MS Baloul","year":"2022","unstructured":"Baloul MS, Yeh VJH, Mukhtar F, Ramachandran D, Traynor MD, Shaikh N, et al. Video commentary & machine learning: tell me what you see, I tell you who you are. J Surg Educ. 2022;79(6):e263\u201372.","journal-title":"J Surg Educ"},{"issue":"8","key":"7321_CR33","doi-asserted-by":"publisher","first-page":"e198363","DOI":"10.1001\/jamanetworkopen.2019.8363","volume":"2","author":"A Winkler-Schwartz","year":"2019","unstructured":"Winkler-Schwartz A, Yilmaz R, Mirchi N, Bissonnette V, Ledwos N, Siyar S, et al. Machine learning identification of surgical and operative factors associated with surgical expertise in virtual reality simulation. JAMA Netw Open. 2019;2(8):e198363.","journal-title":"JAMA Netw Open"},{"issue":"S1","key":"7321_CR34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11548-018-1766-y","volume":"13","author":"S Siyar","year":"2018","unstructured":"Siyar S, Azarnoush H, Rashidi S, Winkler-Schwartz A. Using classifiers to distinguish neurosurgical skill levels in a virtual reality tumor resection task. Int J Comput Assist Radiol Surg. 2018;13(S1):1\u2013273.","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"23","key":"7321_CR35","doi-asserted-by":"publisher","first-page":"e127","DOI":"10.2106\/JBJS.18.01197","volume":"101","author":"V Bissonnette","year":"2019","unstructured":"Bissonnette V, Mirchi N, Ledwos N, Alsidieri G, Winkler-Schwartz A, Del Maestro RF, et al. Artificial intelligence distinguishes surgical training levels in a virtual reality spinal task. J Bone Jt Surg. 2019;101(23):e127.","journal-title":"J Bone Jt Surg"},{"issue":"7","key":"7321_CR36","doi-asserted-by":"publisher","first-page":"5547","DOI":"10.1007\/s00464-022-09707-8","volume":"37","author":"KM Quinn","year":"2023","unstructured":"Quinn KM, Chen X, Runge LT, Pieper H, Renton D, Meara M, et al. The robot doesn\u2019t Lie: real-life validation of robotic performance metrics. Surg Endosc. 2023;37(7):5547\u201352.","journal-title":"Surg Endosc"},{"key":"7321_CR37","doi-asserted-by":"publisher","first-page":"105234","DOI":"10.1016\/j.cmpb.2019.105234","volume":"187","author":"NX Anh","year":"2020","unstructured":"Anh NX, Nataraja RM, Chauhan S. Towards near real-time assessment of surgical skills: A comparison of feature extraction techniques. Comput Methods Programs Biomed. 2020;187:105234.","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"7321_CR38","doi-asserted-by":"publisher","first-page":"100235","DOI":"10.1016\/j.xops.2022.100235","volume":"3","author":"J Ruzicki","year":"2023","unstructured":"Ruzicki J, Holden M, Cheon S, Ungi T, Egan R, Law C. Use of machine learning to assess cataract surgery skill level with tool detection. Ophthalmol Sci. 2023;3(1):100235.","journal-title":"Ophthalmol Sci"},{"issue":"11","key":"7321_CR39","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1007\/s11548-019-01977-3","volume":"14","author":"MS Holden","year":"2019","unstructured":"Holden MS, Xia S, Lia H, Keri Z, Bell C, Patterson L, et al. Machine learning methods for automated technical skills assessment with instructional feedback in ultrasound-guided interventions. Int J Comput Assist Radiol Surg. 2019;14(11):1993\u20132003.","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"2","key":"7321_CR40","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s00464-013-3226-7","volume":"28","author":"I Oropesa","year":"2014","unstructured":"Oropesa I, S\u00e1nchez-Gonz\u00e1lez P, Chmarra MK, Lamata P, P\u00e9rez-Rodr\u00edguez R, Jansen FW, et al. Supervised classification of psychomotor competence in minimally invasive surgery based on instruments motion analysis. Surg Endosc. 2014;28(2):657\u201370.","journal-title":"Surg Endosc"},{"issue":"3","key":"7321_CR41","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1016\/j.jtcvs.2011.11.002","volume":"143","author":"R Kumar","year":"2012","unstructured":"Kumar R, Jog A, Vagvolgyi B, Nguyen H, Hager G, Chen CCG, et al. Objective measures for longitudinal assessment of robotic surgery training. J Thorac Cardiovasc Surg. 2012;143(3):528\u201334.","journal-title":"J Thorac Cardiovasc Surg"},{"key":"7321_CR42","unstructured":"Ariaeinejad A, Samavi DR. A Performance Predictive Model for Emergency Medicine Residents."},{"key":"7321_CR43","doi-asserted-by":"crossref","unstructured":"Amirhajlou L, Sohrabi Z, Alebouyeh MR, Tavakoli N, Haghighi RZ, Hashemi A et al. Application of data mining techniques for predicting residents\u2019 performance on pre\u2013board examinations: A case study. J Educ Health Promot. 2019;8.","DOI":"10.4103\/jehp.jehp_394_18"},{"issue":"3","key":"7321_CR44","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1016\/j.jsurg.2014.11.013","volume":"72","author":"MJ Yost","year":"2015","unstructured":"Yost MJ, Gardner J, Bell RM, Fann SA, Lisk JR, Cheadle WG, et al. Predicting academic performance in surgical training. J Surg Educ. 2015;72(3):491\u20139.","journal-title":"J Surg Educ"},{"issue":"S1","key":"7321_CR45","first-page":"S47","volume":"25","author":"R Woods","year":"2023","unstructured":"Woods R, Spadafore M, Yilmaz Y, Rally V, Russell M, Thoma B, et al. Your comment is not as helpful as it could be\u2026 do you still want to submit?\u2019 using natural Language processing to identify the quality of supervisor narrative comments in competency based medical education. Can J Emerg Med. 2023;25(S1):S47.","journal-title":"Can J Emerg Med"},{"issue":"5","key":"7321_CR46","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1097\/ACM.0000000000005634","volume":"99","author":"M Spadafore","year":"2024","unstructured":"Spadafore M, Yilmaz Y, Rally V, Chan TM, Russell M, Thoma B, et al. Using natural Language processing to evaluate the quality of supervisor narrative comments in Competency-Based medical education. Acad Med. 2024;99(5):534\u201340.","journal-title":"Acad Med"},{"key":"7321_CR47","unstructured":"Zhang R. Automated Assessment of Medical Training Evaluation Text."},{"issue":"2","key":"7321_CR48","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.jsurg.2023.10.007","volume":"81","author":"CY Ryder","year":"2024","unstructured":"Ryder CY, Mott NM, Gross CL, Anidi C, Shigut L, Bidwell SS, et al. Using artificial intelligence to gauge competency on a novel laparoscopic training system. J Surg Educ. 2024;81(2):267\u201374.","journal-title":"J Surg Educ"},{"issue":"2","key":"7321_CR49","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.amjsurg.2020.11.044","volume":"221","author":"CC Stahl","year":"2021","unstructured":"Stahl CC, Jung SA, Rosser AA, Kraut AS, Schnapp BH, Westergaard M, et al. Natural Language processing and entrustable professional activity text feedback in surgery: A machine learning model of resident autonomy. Am J Surg. 2021;221(2):369\u201375.","journal-title":"Am J Surg"},{"issue":"6","key":"7321_CR50","doi-asserted-by":"publisher","first-page":"e72","DOI":"10.1016\/j.jsurg.2021.05.012","volume":"78","author":"QP Solano","year":"2021","unstructured":"Solano QP, Hayward L, Chopra Z, Quanstrom K, Kendrick D, Abbott KL, et al. Natural Language processing and assessment of resident feedback quality. J Surg Educ. 2021;78(6):e72\u20137.","journal-title":"J Surg Educ"},{"issue":"2","key":"7321_CR51","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1213\/ANE.0000000000005265","volume":"132","author":"SE Neves","year":"2021","unstructured":"Neves SE, Chen MJ, Ku CM, Karan S, DiLorenzo AN, Schell RM, et al. Using machine learning to evaluate attending feedback on resident performance. Anesth Analg. 2021;132(2):545\u201355.","journal-title":"Anesth Analg"},{"key":"7321_CR52","unstructured":"Lui A, Chary M, Yoneda N, Parikh S. Tracking resident cognitive maturation with natural language processing. West J Emerg Med., (Lui A, Chary M, Yoneda N, Parikh S.) New York Presbyterian Queens, Flushing, NY, United States):S46."},{"issue":"8","key":"7321_CR53","doi-asserted-by":"publisher","first-page":"1614","DOI":"10.1016\/j.acap.2023.02.012","volume":"23","author":"H Boolchandani","year":"2023","unstructured":"Boolchandani H, Osborn R, Tiyyagura G, Sheares B, Chen L, Phatak UP, et al. Words used in letters of recommendation for pediatric residency applicants: demographic differences and impact on interviews. Acad Pediatr. 2023;23(8):1614\u20139.","journal-title":"Acad Pediatr"},{"issue":"6","key":"7321_CR54","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1016\/j.amjsurg.2021.09.034","volume":"222","author":"D Sarraf","year":"2021","unstructured":"Sarraf D, Vasiliu V, Imberman B, Lindeman B. Use of artificial intelligence for gender bias analysis in letters of recommendation for general surgery residency candidates. Am J Surg. 2021;222(6):1051\u20139.","journal-title":"Am J Surg"},{"issue":"10","key":"7321_CR55","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.1002\/alr.23153","volume":"13","author":"V Vasan","year":"2023","unstructured":"Vasan V, Cheng C, Lerner DK, Signore AD, Schaberg M, Govindaraj S, et al. Letters of recommendations and personal statements for rhinology fellowship: A deep learning linguistic analysis. Int Forum Allergy Rhinol. 2023;13(10):1971\u20133.","journal-title":"Int Forum Allergy Rhinol"},{"issue":"4","key":"7321_CR56","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1016\/j.jsurg.2022.12.002","volume":"80","author":"GM Gray","year":"2023","unstructured":"Gray GM, Williams SA, Bludevich B, Irby I, Chang H, Danielson PD, et al. Examining implicit Bias differences in pediatric surgical fellowship letters of recommendation using natural Language processing. J Surg Educ. 2023;80(4):547\u201355.","journal-title":"J Surg Educ"},{"issue":"11","key":"7321_CR57","doi-asserted-by":"publisher","first-page":"1278","DOI":"10.1097\/ACM.0000000000005352","volume":"98","author":"B Drum","year":"2023","unstructured":"Drum B, Shi J, Peterson B, Lamb S, Hurdle JF, Gradick C. Using natural Language processing and machine learning to identify internal Medicine\u2013Pediatrics residency values in applications. Acad Med. 2023;98(11):1278\u201382.","journal-title":"Acad Med"},{"issue":"11S","key":"7321_CR58","doi-asserted-by":"publisher","first-page":"S54","DOI":"10.1097\/ACM.0000000000004317","volume":"96","author":"J Burk-Rafel","year":"2021","unstructured":"Burk-Rafel J, Reinstein I, Feng J, Kim MB, Miller LH, Cocks PM, et al. Development and validation of a machine Learning-Based decision support tool for residency applicant screening and review. Acad Med. 2021;96(11S):S54\u201361.","journal-title":"Acad Med"},{"issue":"3","key":"7321_CR59","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1080\/10401334.2022.2059664","volume":"35","author":"CA Rees","year":"2023","unstructured":"Rees CA, Ryder HF. Machine learning for the prediction of ranked applicants and matriculants to an internal medicine residency program. Teach Learn Med. 2023;35(3):277\u201386.","journal-title":"Teach Learn Med"},{"issue":"11","key":"7321_CR60","doi-asserted-by":"publisher","first-page":"3582","DOI":"10.1007\/s11606-020-06370-4","volume":"36","author":"JA Summers","year":"2021","unstructured":"Summers JA. Analysis of the impact of step 1 scores on rank order for the NRMP match. J Gen Intern Med. 2021;36(11):3582\u20133.","journal-title":"J Gen Intern Med"},{"issue":"4","key":"7321_CR61","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/S0735-6757(97)90125-X","volume":"15","author":"S Pilon","year":"1997","unstructured":"Pilon S, Tandberg D. Neural network and linear regression models in residency selection. Am J Emerg Med. 1997;15(4):361\u20134.","journal-title":"Am J Emerg Med"},{"issue":"2","key":"7321_CR62","doi-asserted-by":"publisher","first-page":"559","DOI":"10.3171\/2022.5.JNS22558","volume":"138","author":"AV Ortiz","year":"2023","unstructured":"Ortiz AV, Feldman MJ, Yengo-Kahn AM, Roth SG, Dambrino RJ, Chitale RV, et al. Words matter: using natural Language processing to predict neurosurgical residency match outcomes. J Neurosurg. 2023;138(2):559\u201366.","journal-title":"J Neurosurg"},{"issue":"9","key":"7321_CR63","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1097\/ACM.0000000000005210","volume":"98","author":"AU Mahtani","year":"2023","unstructured":"Mahtani AU, Reinstein I, Marin M, Burk-Rafel J. A new tool for holistic residency application review: using natural Language processing of applicant experiences to predict interview invitation. Acad Med. 2023;98(9):1018\u201321.","journal-title":"Acad Med"},{"key":"7321_CR64","doi-asserted-by":"publisher","first-page":"111185","DOI":"10.1016\/j.jclinane.2023.111185","volume":"89","author":"RE Johnstone","year":"2023","unstructured":"Johnstone RE, Neely G, Sizemore DC. Artificial intelligence software can generate residency application personal statements that program directors find acceptable and difficult to distinguish from applicant compositions. J Clin Anesth. 2023;89:111185.","journal-title":"J Clin Anesth"},{"issue":"3","key":"7321_CR65","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1097\/SAP.0000000000003653","volume":"91","author":"V Patel","year":"2023","unstructured":"Patel V, Deleonibus A, Wells MW, Bernard SL, Schwarz GS. Distinguishing authentic voices in the age of ChatGPT: comparing AI-Generated and Applicant-Written personal statements for plastic surgery residency application. Ann Plast Surg. 2023;91(3):324\u20135.","journal-title":"Ann Plast Surg"},{"issue":"1","key":"7321_CR66","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1186\/s12909-022-03872-z","volume":"23","author":"PK Yi","year":"2023","unstructured":"Yi PK, Ray ND, Segall N. A novel use of an artificially intelligent chatbot and a live, synchronous virtual question-and answer session for fellowship recruitment. BMC Med Educ. 2023;23(1):152.","journal-title":"BMC Med Educ"},{"key":"7321_CR67","doi-asserted-by":"crossref","unstructured":"Zhao XX, Wu SP, Wang JY, Gong XY, He XR, Xi MJ et al. Comparison of Multiple Quantitative Evaluation Indices of Theoretical Knowledge and Clinical Practice Skills and Training of Medical Interns in Cardiovascular Imaging Using Blended Teaching and the Case Resource Network Platform (CRNP). Med Sci Monit Int Med J Exp Clin Res. 2020;26(dxw, 9609063):e923836.","DOI":"10.12659\/MSM.923836"},{"issue":"3","key":"7321_CR68","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1016\/j.acap.2021.12.013","volume":"22","author":"C Merritt","year":"2022","unstructured":"Merritt C, Glisson M, Dewan M, Klein M, Zackoff M. Implementation and evaluation of an artificial intelligence driven simulation to improve resident communication with primary care providers. Acad Pediatr. 2022;22(3):503\u20135.","journal-title":"Acad Pediatr"},{"key":"7321_CR69","doi-asserted-by":"crossref","unstructured":"Webb JJ. Proof of Concept: Using ChatGPT to Teach Emergency Physicians How to Break Bad News. Cureus. 2023 May 9 [cited 2024 May 18]; Available from: https:\/\/www.cureus.com\/articles\/154391-proof-of-concept-using-chatgpt-to-teach-emergency-physicians-how-to-break-bad-news","DOI":"10.7759\/cureus.38755"},{"issue":"5","key":"7321_CR70","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1007\/s10459-007-9081-3","volume":"13","author":"GM El Saadawi","year":"2008","unstructured":"El Saadawi GM, Tseytlin E, Legowski E, Jukic D, Castine M, Fine J, et al. A natural Language intelligent tutoring system for training pathologists: implementation and evaluation. Adv Health Sci Educ. 2008;13(5):709\u201322.","journal-title":"Adv Health Sci Educ"},{"issue":"8","key":"7321_CR71","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1016\/j.jacr.2016.03.012","volume":"13","author":"LC Kelahan","year":"2016","unstructured":"Kelahan LC, Fong A, Ratwani RM, Filice RW. Call case dashboard: tracking R1 exposure to High-Acuity cases using natural Language processing. J Am Coll Radiol. 2016;13(8):988\u201391.","journal-title":"J Am Coll Radiol"},{"issue":"4","key":"7321_CR72","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s10278-014-9678-z","volume":"27","author":"H Lin","year":"2014","unstructured":"Lin H, Yang X, Wang WA, Content-Boosted. Collaborative filtering algorithm for personalized training in interpretation of radiological imaging. J Digit Imaging. 2014;27(4):449\u201356.","journal-title":"J Digit Imaging"},{"issue":"5","key":"7321_CR73","doi-asserted-by":"publisher","first-page":"1825","DOI":"10.3390\/jcm12051825","volume":"12","author":"GA Muntean","year":"2023","unstructured":"Muntean GA, Groza A, Marginean A, Slavescu RR, Steiu MG, Muntean V, et al. Artificial intelligence for personalised ophthalmology residency training. J Clin Med. 2023;12(5):1825.","journal-title":"J Clin Med"},{"issue":"1","key":"7321_CR74","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s10278-018-0123-6","volume":"32","author":"H Chen","year":"2019","unstructured":"Chen H, Gangaram V, Shih G. Developing a more responsive radiology resident dashboard. J Digit Imaging. 2019;32(1):81\u201390.","journal-title":"J Digit Imaging"},{"key":"7321_CR75","unstructured":"Nori H, King N, McKinney SM, Carignan D, Horvitz E. Capabilities of GPT-4 on Medical Challenge Problems. 2023."},{"issue":"5","key":"7321_CR76","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1227\/neu.0000000000002551","volume":"93","author":"R Ali","year":"2023","unstructured":"Ali R, Tang OY, Connolly ID, Fridley JS, Shin JH, Zadnik Sullivan PL, et al. Performance of ChatGPT, GPT-4, and Google bard on a neurosurgery oral boards Preparation question bank. Neurosurgery. 2023;93(5):1090\u20138.","journal-title":"Neurosurgery"},{"key":"7321_CR77","doi-asserted-by":"publisher","first-page":"102715","DOI":"10.1016\/j.bas.2023.102715","volume":"4","author":"A Bartoli","year":"2024","unstructured":"Bartoli A, May AT, Al-Awadhi A, Schaller K. Probing artificial intelligence in neurosurgical training: ChatGPT takes a neurosurgical residents written exam. Brain Spine. 2024;4:102715.","journal-title":"Brain Spine"},{"key":"7321_CR78","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.bjps.2023.09.027","volume":"87","author":"R Gupta","year":"2023","unstructured":"Gupta R, Park JB, Herzog I, Yosufi N, Mangan A, Firouzbakht PK, et al. Applying GPT-4 to the plastic surgery inservice training examination. J Plast Reconstr Aesthet Surg. 2023;87:78\u201382.","journal-title":"J Plast Reconstr Aesthet Surg"},{"issue":"12","key":"7321_CR79","doi-asserted-by":"publisher","first-page":"NP1085","DOI":"10.1093\/asj\/sjad130","volume":"43","author":"P Humar","year":"2023","unstructured":"Humar P, Asaad M, Bengur FB, Nguyen V. ChatGPT is equivalent to First-Year plastic surgery residents: evaluation of ChatGPT on the plastic surgery In-Service examination. Aesthet Surg J. 2023;43(12):NP1085\u20139.","journal-title":"Aesthet Surg J"},{"key":"7321_CR80","unstructured":"Holmes J, Ye S, Li Y, Wu SN, Liu Z, Zhao H et al. Evaluating Large Language Models in Ophthalmology."},{"issue":"6","key":"7321_CR81","doi-asserted-by":"publisher","first-page":"1797","DOI":"10.1007\/s00404-023-07185-4","volume":"308","author":"A Cohen","year":"2023","unstructured":"Cohen A, Alter R, Lessans N, Meyer R, Brezinov Y, Levin G. Performance of ChatGPT in Israeli Hebrew OBGYN National residency examinations. Arch Gynecol Obstet. 2023;308(6):1797\u2013802.","journal-title":"Arch Gynecol Obstet"},{"key":"7321_CR82","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.jor.2023.11.056","volume":"50","author":"MG Rizzo","year":"2024","unstructured":"Rizzo MG, Cai N, Constantinescu D. The performance of ChatGPT on orthopaedic in-service training exams: A comparative study of the GPT-3.5 turbo and GPT-4 models in orthopaedic education. J Orthop. 2024;50:70\u20135.","journal-title":"J Orthop"},{"issue":"8","key":"7321_CR83","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1097\/CORR.0000000000002704","volume":"481","author":"ZC Lum","year":"2023","unstructured":"Lum ZC. Can artificial intelligence pass the American board of orthopaedic surgery examination?? orthopaedic residents versus ChatGPT. Clin Orthop. 2023;481(8):1623\u201330.","journal-title":"Clin Orthop"},{"issue":"5","key":"7321_CR84","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1111\/1742-6723.14280","volume":"35","author":"J Smith","year":"2023","unstructured":"Smith J, Choi PM, Buntine P. Will code one day run a code? Performance of Language models on ACEM primary examinations and implications. Emerg Med Australas. 2023;35(5):876\u20138.","journal-title":"Emerg Med Australas"},{"key":"7321_CR85","unstructured":"Mahajan AP, Shabet CL, Smith J, Rudy SF, Kupfer RA, Bohm LA. Assessment of Artificial Intelligence Performance on the Otolaryngology Residency In-Service Exam. OTP Open. 2023;7(4). Available from: https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85178235724&doi=10.1002%2foto2.98&partnerID=40&md5=d472c848d17df9629bd2685f1dc57c32"},{"key":"7321_CR86","doi-asserted-by":"crossref","unstructured":"Cheung BHH, Lau GKK, Wong GTC, Lee EYP, Kulkarni D, Seow CS et al. J Wang editor 2023 ChatGPT versus human in generating medical graduate exam multiple choice questions\u2014A multinational prospective study (Hong Kong S.A.R., Singapore, Ireland, and the united Kingdom). PLoS ONE 18 8 e0290691.","DOI":"10.1371\/journal.pone.0290691"},{"issue":"10","key":"7321_CR87","doi-asserted-by":"publisher","first-page":"e2022779","DOI":"10.1001\/jamanetworkopen.2020.22779","volume":"3","author":"JT Wu","year":"2020","unstructured":"Wu JT, Wong KCL, Gur Y, Ansari N, Karargyris A, Sharma A, et al. Comparison of chest radiograph interpretations by artificial intelligence algorithm vs radiology residents. JAMA Netw Open. 2020;3(10):e2022779.","journal-title":"JAMA Netw Open"},{"issue":"4","key":"7321_CR88","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s10140-020-01767-4","volume":"27","author":"PH Yi","year":"2020","unstructured":"Yi PH, Kim TK, Yu AC, Bennett B, Eng J, Lin CT. Can AI outperform a junior resident? Comparison of deep neural network to first-year radiology residents for identification of pneumothorax. Emerg Radiol. 2020;27(4):367\u201375.","journal-title":"Emerg Radiol"},{"issue":"6","key":"7321_CR89","doi-asserted-by":"publisher","first-page":"e035757","DOI":"10.1136\/bmjopen-2019-035757","volume":"10","author":"C Zhao","year":"2020","unstructured":"Zhao C, Xiao M, Liu H, Wang M, Wang H, Zhang J, et al. Reducing the number of unnecessary biopsies of US-BI-RADS 4a lesions through a deep learning method for residents-in-training: a cross-sectional study. BMJ Open. 2020;10(6):e035757.","journal-title":"BMJ Open"},{"issue":"12","key":"7321_CR90","doi-asserted-by":"publisher","first-page":"e2141096","DOI":"10.1001\/jamanetworkopen.2021.41096","volume":"4","author":"F Homayounieh","year":"2021","unstructured":"Homayounieh F, Digumarthy S, Ebrahimian S, Rueckel J, Hoppe BF, Sabel BO, et al. An artificial Intelligence\u2013Based chest X-ray model on human nodule detection accuracy from a multicenter study. JAMA Netw Open. 2021;4(12):e2141096.","journal-title":"JAMA Netw Open"},{"issue":"9","key":"7321_CR91","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1016\/j.jid.2020.01.019","volume":"140","author":"SS Han","year":"2020","unstructured":"Han SS, Park I, Eun Chang S, Lim W, Kim MS, Park GH, et al. Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin Cancer and predicting treatment options for 134 skin disorders. J Invest Dermatol. 2020;140(9):1753\u201361.","journal-title":"J Invest Dermatol"},{"key":"7321_CR92","doi-asserted-by":"crossref","unstructured":"Feng Y, Sim Zheng Ting J, Xu X, Bee Kun C, Ong Tien En E et al. Irawan Tan Wee Jun H,. Deep Neural Network Augments Performance of Junior Residents in Diagnosing COVID-19 Pneumonia on Chest Radiographs. Diagnostics. 2023;13(8):1397.","DOI":"10.3390\/diagnostics13081397"},{"issue":"3","key":"7321_CR93","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1111\/j.1475-097X.2006.00669.x","volume":"26","author":"S Olsson","year":"2006","unstructured":"Olsson S, Ohlsson M, \u00d6hlin H, Dzaferagic S, Nilsson M, Sandkull P, et al. Decision support for the initial triage of patients with acute coronary syndromes. Clin Physiol Funct Imaging. 2006;26(3):151\u20136.","journal-title":"Clin Physiol Funct Imaging"},{"issue":"3","key":"7321_CR94","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1016\/j.jaad.2019.07.016","volume":"82","author":"MA Marchetti","year":"2020","unstructured":"Marchetti MA, Liopyris K, Dusza SW, Codella NCF, Gutman DA, Helba B, et al. Computer algorithms show potential for improving dermatologists\u2019 accuracy to diagnose cutaneous melanoma: results of the international skin imaging collaboration 2017. J Am Acad Dermatol. 2020;82(3):622\u20137.","journal-title":"J Am Acad Dermatol"},{"key":"7321_CR95","doi-asserted-by":"crossref","unstructured":"Paul SK, Kim CU, Shieh D, Zhou XY, Pan I, Mehra AA et al. Impact of an Artificial Intelligence Algorithm on Diabetic Retinopathy Grading by Ophthalmology Residents. medRxiv. 2023;((Paul S.K., samantha.paul2@uhhospitals.org; Kim C.U.; Shieh D.; Mehra A.A.; Sobol W.M.) Department of Ophthalmology, University Hospitals Cleveland Medical Center, Case Western Reserve University, School of Medicine, Cleveland, OH, United States(Zhou X.Y.). Available from: https:\/\/www.embase.com\/search\/results?subaction=viewrecord&id=L2026945804&from=export","DOI":"10.1101\/2023.08.05.23293692"},{"key":"7321_CR96","doi-asserted-by":"publisher","first-page":"1053079","DOI":"10.3389\/fcell.2022.1053079","volume":"10","author":"Z Fang","year":"2022","unstructured":"Fang Z, Xu Z, He X, Han W. Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program. Front Cell Dev Biol. 2022;10:1053079.","journal-title":"Front Cell Dev Biol"},{"issue":"6","key":"7321_CR97","doi-asserted-by":"publisher","first-page":"3066","DOI":"10.1007\/s00330-019-06652-4","volume":"30","author":"JH Lee","year":"2020","unstructured":"Lee JH, Ha EJ, Kim D, Jung YJ, Heo S, Jang Y, ho, et al. Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training. Eur Radiol. 2020;30(6):3066\u201372.","journal-title":"Eur Radiol"},{"issue":"11","key":"7321_CR98","doi-asserted-by":"publisher","first-page":"8241","DOI":"10.1007\/s00330-023-10043-1","volume":"33","author":"G Chassagnon","year":"2023","unstructured":"Chassagnon G, Billet N, Rutten C, Toussaint T, Cassius De Linval Q, Collin M, et al. Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation. Eur Radiol. 2023;33(11):8241\u201350.","journal-title":"Eur Radiol"},{"issue":"1","key":"7321_CR99","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s10278-022-00713-9","volume":"36","author":"C Shah","year":"2022","unstructured":"Shah C, Davtyan K, Nasrallah I, Bryan RN, Mohan S. Artificial Intelligence-Powered clinical decision support and simulation platform for radiology trainee education. J Digit Imaging. 2022;36(1):11\u20136.","journal-title":"J Digit Imaging"},{"issue":"4","key":"7321_CR100","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1016\/j.jsurg.2022.01.013","volume":"79","author":"R Thanawala","year":"2022","unstructured":"Thanawala R, Jesneck J, Shelton J, Rhee R, Seymour NE. Overcoming systems factors in case logging with artificial intelligence tools. J Surg Educ. 2022;79(4):1024\u201330.","journal-title":"J Surg Educ"},{"issue":"1","key":"7321_CR101","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1093\/jamia\/ocab253","volume":"29","author":"JJ Gong","year":"2021","unstructured":"Gong JJ, Soleimani H, Murray SG, Adler-Milstein J. Characterizing styles of clinical note production and relationship to clinical work hours among first-year residents. J Am Med Inf Assoc. 2021;29(1):120\u20137.","journal-title":"J Am Med Inf Assoc"},{"issue":"S2","key":"7321_CR102","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1186\/s12911-019-0759-2","volume":"19","author":"Y Gao","year":"2019","unstructured":"Gao Y, Gu L, Wang Y, Wang Y, Yang F. Constructing a Chinese electronic medical record corpus for named entity recognition on resident admit notes. BMC Med Inf Decis Mak. 2019;19(S2):56.","journal-title":"BMC Med Inf Decis Mak"},{"issue":"1","key":"7321_CR103","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.1109\/TVCG.2023.3326929","volume":"30","author":"Y Ouyang","year":"2024","unstructured":"Ouyang Y, Wu Y, Wang H, Zhang C, Cheng F, Jiang C, et al. Leveraging historical medical records as a proxy via multimodal modeling and visualization to enrich medical diagnostic learning. IEEE Trans Vis Comput Graph. 2024;30(1):1238\u201348.","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"7321_CR104","doi-asserted-by":"crossref","unstructured":"Dimitroyannis R, Thodupunoori S, Polster SP, Das P, Roxbury CR. Residency education practices in endoscopic skull base surgery. J Neurol Surg Part B Skull Base. 2023;a\u20132226.","DOI":"10.1055\/s-0044-1780378"},{"issue":"12","key":"7321_CR105","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1111\/medu.14593","volume":"55","author":"J Andrews","year":"2021","unstructured":"Andrews J, Chartash D, Hay S. Gender bias in resident evaluations: natural Language processing and competency evaluation. Med Educ. 2021;55(12):1383\u20137.","journal-title":"Med Educ"},{"issue":"3","key":"7321_CR106","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1093\/jamia\/ocad232","volume":"31","author":"TT Bruny\u00e9","year":"2024","unstructured":"Bruny\u00e9 TT, Booth K, Hendel D, Kerr KF, Shucard H, Weaver DL, et al. Machine learning classification of diagnostic accuracy in pathologists interpreting breast biopsies. J Am Med Inf Assoc. 2024;31(3):552\u201362.","journal-title":"J Am Med Inf Assoc"},{"issue":"11","key":"7321_CR107","doi-asserted-by":"publisher","first-page":"2005","DOI":"10.1007\/s11548-019-01953-x","volume":"14","author":"R DiPietro","year":"2019","unstructured":"DiPietro R, Ahmidi N, Malpani A, Waldram M, Lee GI, Lee MR, et al. Segmenting and classifying activities in robot-assisted surgery with recurrent neural networks. Int J Comput Assist Radiol Surg. 2019;14(11):2005\u201320.","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"7321_CR108","unstructured":"Mathematics and Science Academy at the University of Texas Rio Grande Valley, Edinburg TX, Tippur A. Bridging the Gap: Integrating Artificial Intelligence into Medical Education. DHR Proc. 2023 [cited 2025 Jan 22]; Available from: https:\/\/dhrproceedings.org\/index.php\/DHRP\/article\/view\/94\/58"},{"key":"7321_CR109","doi-asserted-by":"publisher","first-page":"100127","DOI":"10.1016\/j.caeai.2023.100127","volume":"4","author":"J Southworth","year":"2023","unstructured":"Southworth J, Migliaccio K, Glover J, Glover J, Reed D, McCarty C, et al. Developing a model for AI across the curriculum: transforming the higher education landscape via innovation in AI literacy. Comput Educ Artif Intell. 2023;4:100127.","journal-title":"Comput Educ Artif Intell"},{"issue":"6","key":"7321_CR110","doi-asserted-by":"publisher","first-page":"2046","DOI":"10.1016\/j.jsurg.2021.06.013","volume":"78","author":"KL Abbott","year":"2021","unstructured":"Abbott KL, George BC, Sandhu G, Harbaugh CM, Gauger PG, \u00d6tle\u015f E, et al. Natural Language processing to estimate clinical competency committee ratings. J Surg Educ. 2021;78(6):2046\u201351.","journal-title":"J Surg Educ"},{"issue":"10","key":"7321_CR111","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1097\/ACM.0000000000004153","volume":"96","author":"E \u00d6tle\u015f","year":"2021","unstructured":"\u00d6tle\u015f E, Kendrick DE, Solano QP, Schuller M, Ahle SL, Eskender MH, et al. Using natural Language processing to automatically assess feedback quality: findings from 3 surgical residencies. Acad Med. 2021;96(10):1457\u201360.","journal-title":"Acad Med"},{"issue":"4","key":"7321_CR112","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1097\/ACM.0000000000005115","volume":"98","author":"GJ Booth","year":"2023","unstructured":"Booth GJ, Ross B, Cronin WA, McElrath A, Cyr KL, Hodgson JA, et al. Competency-Based assessments: leveraging artificial intelligence to predict subcompetency content. Acad Med. 2023;98(4):497\u2013504.","journal-title":"Acad Med"},{"issue":"1","key":"7321_CR113","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1097\/SIH.0000000000000704","volume":"19","author":"DC Brown","year":"2024","unstructured":"Brown DC, Gonzalez-Vargas JM, Tzamaras HM, Sinz EH, Ng PK, Yang MX, et al. Evaluating the impact of assessment metrics for simulated central venous catheterization training. Simul Healthc J Soc Simul Healthc. 2024;19(1):27\u201334.","journal-title":"Simul Healthc J Soc Simul Healthc"},{"issue":"5","key":"7321_CR114","doi-asserted-by":"publisher","first-page":"2123","DOI":"10.1007\/s00423-022-02505-9","volume":"407","author":"K Ebina","year":"2022","unstructured":"Ebina K, Abe T, Hotta K, Higuchi M, Furumido J, Iwahara N, et al. Objective evaluation of laparoscopic surgical skills in wet lab training based on motion analysis and machine learning. Langenbecks Arch Surg. 2022;407(5):2123\u201332.","journal-title":"Langenbecks Arch Surg"},{"issue":"11","key":"7321_CR115","doi-asserted-by":"publisher","first-page":"1516","DOI":"10.1016\/j.jsurg.2023.06.004","volume":"80","author":"RS Gates","year":"2023","unstructured":"Gates RS, Marcotte K, Moreci R, George BC, Kim GJ, Kraft KH, et al. Association of gender and operative feedback quality in surgical residents. J Surg Educ. 2023;80(11):1516\u201321.","journal-title":"J Surg Educ"},{"issue":"4","key":"7321_CR116","doi-asserted-by":"publisher","first-page":"e14780","DOI":"10.1016\/j.heliyon.2023.e14780","volume":"9","author":"J Hern\u00e1ndez-Rodr\u00edguez","year":"2023","unstructured":"Hern\u00e1ndez-Rodr\u00edguez J, Rodr\u00edguez-Conde MJ, Santos-S\u00e1nchez J\u00c1, Cabrero-Fraile FJ. Development and validation of an educational software based in artificial neural networks for training in radiology (JORCAD) through an interactive learning activity. Heliyon. 2023;9(4):e14780.","journal-title":"Heliyon"},{"key":"7321_CR117","doi-asserted-by":"crossref","unstructured":"Jalali S, Stroulia E, Foster S, Persad A, Shi D, Forgie S, LiveBook. Competence Assessment with Virtual-Patient Simulations. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS). Thessaloniki: IEEE; 2017 [cited 2024 May 18]. pp. 47\u201352. Available from: http:\/\/ieeexplore.ieee.org\/document\/8104155\/","DOI":"10.1109\/CBMS.2017.133"},{"issue":"3","key":"7321_CR118","doi-asserted-by":"publisher","first-page":"577","DOI":"10.4300\/JGME-D-13-00267.1","volume":"6","author":"R Madhavan","year":"2014","unstructured":"Madhavan R, Tang C, Bhattacharya P, Delly F, Basha MM. Evaluation of Documentation patterns of trainees and supervising physicians using data mining. J Grad Med Educ. 2014;6(3):577\u201380.","journal-title":"J Grad Med Educ"},{"issue":"3","key":"7321_CR119","doi-asserted-by":"publisher","first-page":"344","DOI":"10.4300\/JGME-D-18-00839.1","volume":"11","author":"MM Vasoya","year":"2019","unstructured":"Vasoya MM, Shivakumar A, Pappu S, Murphy CP, Pei Y, Bricker DA, et al. ReadMI: an innovative app to support training in motivational interviewing. J Grad Med Educ. 2019;11(3):344\u20136.","journal-title":"J Grad Med Educ"}],"container-title":["BMC Medical Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12909-025-07321-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12909-025-07321-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12909-025-07321-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T09:10:19Z","timestamp":1752138619000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmededuc.biomedcentral.com\/articles\/10.1186\/s12909-025-07321-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,20]]},"references-count":119,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["7321"],"URL":"https:\/\/doi.org\/10.1186\/s12909-025-07321-5","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5193696\/v1","asserted-by":"object"}]},"ISSN":["1472-6920"],"issn-type":[{"value":"1472-6920","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,20]]},"assertion":[{"value":"2 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2025","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This article has been updated to amend the license information.","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trial number"}}],"article-number":"730"}}