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                   DOI  PubMed  PMC
               59.      Bencteux V, Saibro G, Shlomovitz E, et al. Automatic task recognition in a flexible endoscopy benchtop trainer with semi-supervised
                   learning. Int J Comput Assist Radiol Surg. 2020;15:1585-95.  DOI
               60.      Eckhoff JA, Rosman G, Altieri MS, et al. SAGES consensus recommendations on surgical video data use, structure, and exploration
                   (for research in artificial intelligence, clinical quality improvement, and surgical education). Surg Endosc. 2023;37:8690-707.  DOI
                   PubMed  PMC
               61.      Mutter D, Vix M, Dallemagne B, Perretta S, Leroy J, Marescaux J. WeBSurg: an innovative educational Web site in minimally
                   invasive surgery - principles and results. Surg Innov. 2011;18:8-14.  DOI  PubMed
               62.      Seenivasan L, Islam M, Krishna AK, Ren H. Surgical-VQA: visual question answering in surgical scenes using transformer. arXiv
                   2022, arXiv:2206.11053. Available from: https://doi.org/10.48550/arXiv.2206.11053. [Last accessed on 17 Apr 2025]
               63.      Browne R, Gull K, Hurley CM, Sugrue RM, O’Sullivan JB. ChatGPT-4 can help hand surgeons communicate better with patients. J
                   Hand Surg Glob Online. 2024;6:436-8.  DOI  PubMed  PMC
               64.      Ruksakulpiwat S, Kumar A, Ajibade A. Using ChatGPT in medical research: current status and future directions. J Multidiscip
                   Healthc. 2023;16:1513-20.  DOI  PubMed  PMC
               65.      Lai H, Ge L, Sun M, et al. Assessing the risk of bias in randomized clinical trials with large language models. JAMA Netw Open.
                   2024;7:e2412687.  DOI  PubMed  PMC
               66.      Kirova VD, Ku CS, Laracy JR, Marlowe TJ. Software engineering education must adapt and evolve for an LLM environment. In:
                   Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, New York, USA. 2024. Association for
                   Computing Machinery; 2024. pp. 666-72.  DOI
               67.      Steyerberg EW, Harrell FE Jr. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol.
                   2016;69:245-7.  DOI  PubMed  PMC
               68.      Daes J, Felix E. Critical view of the myopectineal orifice. Ann Surg. 2017;266:e1-2.  DOI  PubMed
               69.      Barberio M, Collins T, Bencteux V, et al. Deep learning analysis of in vivo hyperspectral images for automated intraoperative nerve
                   detection. Diagnostics. 2021;11:1508.  DOI  PubMed  PMC
               70.      Wang C. Calibration in deep learning: a survey of the state-of-the-art. arXiv 2023, arXiv:2308.01222. Available from: https://doi.org/
                   10.48550/arXiv.2308.01222. [Last accessed on 17 Apr 2025]
               71.      Borys K, Schmitt YA, Nauta M, et al. Explainable AI in medical imaging: an overview for clinical practitioners - Saliency-based XAI
                   approaches. Eur J Radiol. 2023;162:110787.  DOI
               72.      Alberdi E, Povykalo A, Strigini L, Ayton P. Effects of incorrect computer-aided detection (CAD) output on human decision-making in
                   mammography. Acad Radiol. 2004;11:909-18.  DOI  PubMed
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