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References

  1. Bellman RE. An Introduction to Artificial Intelligence: Can Computers Think? San Francisco: Boyd & Fraser; 1978.
  2. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial Intelligence in Surgery: Promises and Perils. Ann Surg. 2018 Jul;268(1):70-76. doi: 10.1097.
  3. Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402-2410. doi:10.1001/jama.2016.17216
  4. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118. doi:10.1038/nature21056
  5. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94. doi:10.1038
  6. Ericsson KA, Hoffman RR, Kozbelt A, Williams AM. The Cambridge Handbook of Expertise and Expert Performance. Cambridge, United Kingdom: Cambridge University Press; 2018.
  7. Mirchi N, Bissonnette V, Ledwos N, et al. Artificial Neural Networks to Assess Virtual Reality Anterior Cervical Discectomy Performance. Oper Neurosurg (Hagerstown). 2020;19(1):65-75. doi:10.1093/ons/opz359
  8. Winkler-Schwartz A, Bissonnette V, Mirchi N, et al. Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation. J Surg Educ. 2019;76(6):1681-1690. doi:10.1016/j.jsurg.2019.05.015
  9. Winkler-Schwartz A, Yilmaz R, Mirchi N, Bissonnette V, Ledwos N, Siyar S, Azarnoush H, Karlik B, Del Maestro R. Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation. JAMA Netw Open. 2019 Aug 2;2(8):e198363. doi: 10.1001/jamanetworkopen.2019.8363.
  10. Malpani A, Vedula SS, Lin HC, Hager GD, Taylor RH. Effect of real-time virtual reality-based teaching cues on learning needle passing for robot-assisted minimally invasive surgery: a randomized controlled trial. Int J Comput Assist Radiol Surg. 2020;15(7):1187-1194. doi:10.1007/s11548-020-02156-5
  11. Birkmeyer JD, Finks JF, O'Reilly A, et al. Surgical skill and complication rates after bariatric surgery. N Engl J Med. 2013;369(15):1434-1442. doi:10.1056/NEJMsa1300625
  12. Lendvay TS, White L, Kowalewski T. Crowdsourcing to Assess Surgical Skill. JAMA Surg. 2015;150(11):1086-1087. doi:10.1001/jamasurg.2015.2405
  13. Curtis NJ, Foster JD, Miskovic D, et al. Association of Surgical Skill Assessment With Clinical Outcomes in Cancer Surgery. JAMA Surg. 2020;155(7):590-598. doi:10.1001/jamasurg.2020.1004
  14. Hashimoto DA, Rosman G, Witkowski ER, et al. Computer Vision Analysis of Intraoperative Video: Automated Recognition of Operative Steps in Laparoscopic Sleeve Gastrectomy. Ann Surg. 2019;270(3):414-421. doi:10.1097/SLA.0000000000003460
  15. Korndorffer JR, Hawn MT, Spain DA, et al. Situating Artificial Intelligence In Surgery: A Focus On Disease Severity. Ann Surg. 2020 Sept;272(3):523-528.
  16. Hung AJ, Chen J, Che Z, et al. Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes. J Endourol. 2018;32(5):438-444. doi:10.1089/end.2018.0035
  17. Hung AJ, Oh PJ, Chen J, et al. Experts vs super-experts: differences in automated performance metrics and clinical outcomes for robot-assisted radical prostatectomy. BJU Int. 2019;123(5):861-868. doi:10.1111/bju.14599
  18. Russell. Artificial Intelligence: A Modern Approach, Global Edition. Pearson; 2016.
  19. Madani A, Watanabe Y, Vassiliou M, et al. Defining competencies for safe thyroidectomy: An international Delphi consensus. Surgery. 2016;159(1):86-101. doi:10.1016/j.surg.2015.07.039
  20. Madani A, Watanabe Y, Feldman LS, et al. Expert Intraoperative Judgment and Decision-Making: Defining the Cognitive Competencies for Safe Laparoscopic Cholecystectomy. J Am Coll Surg. 2015;221(5):931-940.e8. doi:10.1016/j.jamcollsurg.2015.07.450
  21. Madani A, Grover K, Kuo JH, et al. Defining the competencies for laparoscopic transabdominal adrenalectomy: An investigation of intraoperative behaviors and decisions of experts. Surgery. 2020;167(1):241-249. doi:10.1016/j.surg.2019.03.035
  22. Madani A, Grover K, Watanabe Y. Measuring and Teaching Intraoperative Decision-Making Using the Visual Concordance Test: Deliberate Practice of Advanced Cognitive Skills [published online ahead of print, 2019 Nov 13]. JAMA Surg. 2019;10.1001/jamasurg.2019.4415. doi:10.1001/jamasurg.2019.4415
  23. Madani A, Keller DS. Assessing and improving intraoperative judgement. Br J Surg. 2019;106(13):1723-1725. doi:10.1002/bjs.11386
  24. Conati C, Porayska-Pomsta K, Mavrikis M. AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling. 2018. arXiv:1807.00154
  25. Gordon L, Grantcharov T, Rudzicz F. Explainable Artificial Intelligence for Safe Intraoperative Decision Support. JAMA Surg. 2019;154(11):1064-1065. doi:10.1001/jamasurg.2019.2821
  26. Bittner JG 4th, Logghe HJ, Kane ED, et al. A Society of Gastrointestinal and Endoscopic Surgeons (SAGES) statement on closed social media (Facebook®) groups for clinical education and consultation: issues of informed consent, patient privacy, and surgeon protection. Surg Endosc. 2019;33(1):1-7. doi:10.1007/s00464-018-6569-2
  27. McCulloch P, Altman DG, Campbell WB, et al. No surgical innovation without evaluation: the IDEAL recommendations. Lancet. 2009;374(9695):1105-1112. doi:10.1016/S0140-6736(09)61116-8
  28. Schaffter T, Buist DSM, Lee CI, et al. Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Netw Open. 2020;3(3):e200265. Published 2020 Mar 2. doi:10.1001/jamanetworkopen.2020.0265

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