Webster CS. Artificial intelligence and the adoption of new technology in medical education. Med Educ. 2021;55:6–7. https://doi.org/10.1111/medu.14409.
Lazarus MD, Truong M, Douglas P, et al. Artificial intelligence and clinical anatomical education: Promises and perils. Anat Sci Educ. 2022. https://doi.org/10.1002/ase.2221.
Eysenbach G. The Role of ChatGPT, Generative language models, and Artificial Intelligence in medical education: a conversation with ChatGPT and a call for papers. JMIR Med Educ. 2023;9: e46885. https://doi.org/10.2196/46885.
Gilson A, Safranek CW, Huang T, et al. How does ChatGPT perform on the United States Medical Licensing Examination? The implications of large language models for medical education and knowledge assessment. JMIR Med Educ. 2023;9: e45312. https://doi.org/10.2196/45312.
Kung TH, Cheatham M, Medenilla A, et al. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2: e0000198. https://doi.org/10.1371/journal.pdig.0000198.
Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284:574–82. https://doi.org/10.1148/radiol.2017162326.
Ludwig CA, Perera C, Myung D, et al. Automatic identification of referral-warranted diabetic retinopathy using deep learning on mobile phone images. Transl Vis Sci Technol. 2020;9:60. https://doi.org/10.1167/tvst.9.2.60.
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115–8. https://doi.org/10.1038/nature21056.
Haenssle HA, Fink C, Schneiderbauer R, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29:1836–42. https://doi.org/10.1093/annonc/mdy166.
Nemati S, Holder A, Razmi F, et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med. 2018;46:547–53. https://doi.org/10.1097/CCM.0000000000002936.
Scheetz J, Rothschild P, McGuinness M, et al. A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Sci Rep. 2021;11:5193–5. https://doi.org/10.1038/s41598-021-84698-5.
Park CJ, Yi PH, Siegel EL. Medical student perspectives on the impact of Artificial Intelligence on the practice of medicine. Curr Probl Diagn Radiol. 2021;50:614–9. https://doi.org/10.1067/j.cpradiol.2020.06.011.
Abdellatif H, Al Mushaiqri M, Albalushi H, et al. Teaching, learning and assessing anatomy with Artificial Intelligence: the road to a better future. Int J Environ Res Public Health. 2022;19:14209. https://doi.org/10.3390/ijerph192114209doi:10.3390/ijerph192114209.
Kirubarajan A, Young D, Khan S, et al. Artificial Intelligence and surgical education: a systematic scoping review of interventions. J Surg Educ. 2022;79:500–15. https://doi.org/10.1016/j.jsurg.2021.09.012.
Bilgic E, Gorgy A, Yang A, et al. Exploring the roles of artificial intelligence in surgical education: a scoping review. Am J Surg. 2022;224:205–16. https://doi.org/10.1016/j.amjsurg.2021.11.023.
Meskó B, Görög M. A short guide for medical professionals in the era of artificial intelligence. NPJ Digit Med 2020;3:126,z. eCollection 2020. https://doi.org/10.1038/s41746-020-00333-z.
Tricco AC, Lillie E, Zarin W, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169:467–73. https://doi.org/10.7326/M18-0850.
Methley AM, Campbell S, Chew-Graham C, et al. PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Serv Res. 2014;14:579. https://doi.org/10.1186/s12913-014-0579-0.
Alonso-Silverio G, Pérez-Escamirosa F, Bruno-Sanchez R, et al. Development of a laparoscopic box trainer based on open source hardware and Artificial Intelligence for objective assessment of surgical psychomotor skills. Surg Innov. 2018;25:380–8. https://doi.org/10.1177/1553350618777045.
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. https://doi.org/10.1016/j.cmpb.2019.105234.
Azari DP, Frasier LL, Quamme SRP, et al. Modeling surgical technical skill using expert assessment for automated computer rating. Ann Surg. 2019;269:574–81. https://doi.org/10.1097/SLA.0000000000002478.
Bissonnette V, Mirchi N, Ledwos N, et al. Artificial Intelligence distinguishes surgical training levels in a virtual reality spinal task. J Bone Joint Surg Am. 2019;101: e127. https://doi.org/10.2106/JBJS.18.01197.
Davids J, Makariou S, Ashrafian H, et al. Automated vision-based microsurgical skill analysis in neurosurgery using deep learning: development and preclinical validation. World Neurosurg. 2021;149:e669–86. https://doi.org/10.1016/j.wneu.2021.01.117.
Fard MJ, Ameri S, Darin Ellis R, et al. Automated robot-assisted surgical skill evaluation: predictive analytics approach. Int J Med Robot. 2018;14. https://doi.org/10.1002/rcs.1850.
Ismail Fawaz H, Forestier G, Weber J, et al. Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks. Int J Comput Assist Radiol Surg. 2019;14:1611–7. https://doi.org/10.1007/s11548-019-02039-4.
Fazlollahi AM, Bakhaidar M, Alsayegh A, et al. Effect of Artificial Intelligence tutoring vs expert instruction on learning simulated surgical skills among medical students: a randomized clinical trial. JAMA Netw Open. 2022;5: e2149008. https://doi.org/10.1001/jamanetworkopen.2021.49008.
Fekri P, Dargahi J, Zadeh M. Deep learning-based haptic guidance for surgical skills transfer. Front Robot AI. 2021;7: 586707. https://doi.org/10.3389/frobt.2020.586707.
French A, Lendvay TS, Sweet RM, et al. Predicting surgical skill from the first N seconds of a task: value over task time using the isogony principle. Int J Comput Assist Radiol Surg. 2017;12:1161–70. https://doi.org/10.1007/s11548-017-1606-5.
Funke I, Mees ST, Weitz J, et al. Video-based surgical skill assessment using 3D convolutional neural networks. Int J Comput Assist Radiol Surg. 2019;14:1217–25. https://doi.org/10.1007/s11548-019-01995-1.
Julian D, Smith R. Developing an intelligent tutoring system for robotic-assisted surgery instruction. Int J Med Robot. 2019;15: e2037. https://doi.org/10.1002/rcs.2037.
Khalid S, Goldenberg M, Grantcharov T, et al. Evaluation of deep learning models for identifying surgical actions and measuring performance. JAMA Netw Open. 2020;3: e201664. https://doi.org/10.1001/jamanetworkopen.2020.1664.
Kowalewski K, Garrow CR, Schmidt MW, et al. Sensor-based machine learning for workflow detection and as key to detect expert level in laparoscopic suturing and knot-tying. Surg Endosc. 2019;33:3732–40. https://doi.org/10.1007/s00464-019-06667-4.
Lavanchy JL, Zindel J, Kirtac K, et al. Automation of surgical skill assessment using a three-stage machine learning algorithm. Sci Rep. 2021;11:5197–206. https://doi.org/10.1038/s41598-021-84295-6.
Ledwos N, Mirchi N, Yilmaz R, et al. Assessment of learning curves on a simulated neurosurgical task using metrics selected by artificial intelligence. J Neurosurg. 2022;137:1160–71. https://doi.org/10.3171/2021.12.JNS211563.
Mirchi N, Bissonnette V, Yilmaz R, et al. The virtual operative assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLoS ONE. 2020;15. https://doi.org/10.1371/journal.pone.0229596.
Mirchi N, Bissonnette V, Ledwos N, et al. Artificial neural networks to assess virtual reality anterior cervical discectomy performance. Oper Neurosurg (Hagerstown). 2020;19:65–75. https://doi.org/10.1093/ons/opz359.
Nakawala H, Ferrigno G, De Momi E. Development of an intelligent surgical training system for thoracentesis. Artif Intell Med. 2018;84:50–63. https://doi.org/10.1016/j.artmed.2017.10.004.
Nguyen XA, Ljuhar D, Pacilli M, et al. Surgical skill levels: classification and analysis using deep neural network model and motion signals. Comput Methods Programs Biomed. 2019;177:1–8. https://doi.org/10.1016/j.cmpb.2019.05.008.oq.
Oquendo YA, Riddle EW, Hiller D, et al. Automatically rating trainee skill at a pediatric laparoscopic suturing task. Surg Endosc. 2018;32:1840–57. https://doi.org/10.1007/s00464-017-5873-6.
Reich A, Mirchi N, Yilmaz R, et al. Artificial neural network approach to competency-based training using a virtual reality neurosurgical simulation. Oper Neurosurg (Hagerstown). 2022;23:31–9. https://doi.org/10.1227/ons.0000000000000173.
Rhienmora P, Haddawy P, Suebnukarn S, et al. Intelligent dental training simulator with objective skill assessment and feedback. Artif Intell Med. 2011;52:115–21. https://doi.org/10.1016/j.artmed.2011.04.003.
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