REFERENCES
1. Gumbs AA, Alexander F, Karcz K, et al. White paper: definitions of artificial intelligence and autonomous actions in clinical surgery. Art Int Surg 2022;2:93-100.
2. Maier-Hein L, Eisenmann M, Sarikaya D, et al. Surgical data science - from concepts toward clinical translation. Med Image Anal 2022;76:102306.
3. Levin M, McKechnie T, Kruse CC, Aldrich K, Grantcharov TP, Langerman A. Surgical data recording in the operating room: a systematic review of modalities and metrics. Br J Surg 2021;108:613-21.
4. Shah P, Thornton I, Turrin D, Hipskind JE. Informed consent. Available from: https://www.ncbi.nlm.nih.gov/books/NBK430827/. [Last accessed on 16 Jan 2023].
5. Dalen ASHM, Legemaate J, Schlack WS, Legemate DA, Schijven MP. Legal perspectives on black box recording devices in the operating environment. Br J Surg 2019;106:1433-41.
6. Chenthara S, Ahmed K, Wang H, Whittaker F, Chen Z. Healthchain: a novel framework on privacy preservation of electronic health records using blockchain technology. PLoS One 2020;15:e0243043.
7. Madani A, Namazi B, Altieri MS, et al. Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Ann Surg 2022;276:363-9.
8. Mascagni P, Alapatt D, Sestini L, et al. Computer vision in surgery: from potential to clinical value. NPJ Digit Med 2022;5:163.
10. Shiroshita H, Inomata M, Akira S, et al. Current status of endoscopic surgery in Japan: the 15th National Survey of Endoscopic Surgery by the Japan Society for Endoscopic Surgery. Asian J Endosc Surg 2022;15:415-26.
11. Prigoff JG, Sherwin M, Divino CM. Ethical recommendations for video recording in the operating room. Ann Surg 2016;264:34-5.
12. D’Acquisto G, Domingo-Ferrer J, Kikiras P, Torra V, de Montjoye YA, Bourka A. Privacy by design in big data: an overview of privacy enhancing technologies in the era of big data analytics. 2015. Available from: https://doi.org/10.2824/641480. [Last accessed on 16 Jan 2024]
13. Filicori F, Addison P. Intellectual property and data ownership in the age of video recording in the operating room. Surg Endosc 2022;36:3772-4.
14. Li L, Fan Y, Tse M, Lin KY. A review of applications in federated learning. Comput Ind Eng 2020;149:106854.
15. Li T, Sahu AK, Talwalkar A, Smith V. Federated learning: challenges, methods, and future directions. IEEE Signal Process Mag 2020;37:50-60.
16. Ahmadi SA, Sadat H, Scheufler KM, Steiger HJ, Weber B, Beez T. Malpractice claims in spine surgery in Germany: a 5-year analysis. Spine J 2019;19:1221-31.
17. Morton JM, Khoury H, Brethauer SA, et al. First report from the American Society of Metabolic and Bariatric Surgery closed-claims registry: prevalence, causes, and lessons learned from bariatric surgery medical malpractice claims. Surg Obes Relat Dis 2022;18:943-7.
18. Delaunay F, Delaunay T, Van Vyve E, Cardin JL. Club Coelio. Analysis of malpractice claims: the Franco-Belgian “Cœlio Club” experience. J Visc Surg 2019;156:S33-9.
19. De Ravin E, Sell EA, Newman JG, Rajasekaran K. Medical malpractice in robotic surgery: a Westlaw database analysis. J Robot Surg 2023;17:191-6.
20. Douglas RN, Stephens LS, Posner KL, et al. Communication failures contributing to patient injury in anaesthesia malpractice claims. Br J Anaesth 2021;127:470-8.
21. Humphrey KE, Sundberg M, Milliren CE, Graham DA, Landrigan CP. Frequency and nature of communication and handoff failures in medical malpractice claims. J Patient Saf 2022;18:130-7.
