REFERENCES
1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44-56.
2. Loftus TJ, Tighe PJ, Filiberto AC, et al. Artificial intelligence and surgical decision-making. JAMA Surg. 2020;155:148-58.
3. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268:70-6.
4. Ryhänen J, Wong GC, Anttila T, Chung KC. Overview of artificial intelligence in hand surgery. J Hand Surg Eur Vol. 2025;50:738-51.
6. Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016 Jun 27-30; Las Vegas, NV, USA. Piscataway (NJ): IEEE; 2016. pp. 779-88.
7. Yang TH, Horng MH, Li RS, Sun YN. Scaphoid fracture detection by using convolutional neural network. Diagnostics. 2022;12:895.
8. Kalmet PHS, Sanduleanu S, Primakov S, et al. Deep learning in fracture detection: a narrative review. Acta Orthop. 2020;91:215-20.
9. Dababneh S, Colivas J, Dababneh N, Efanov JI. Artificial intelligence as an adjunctive tool in hand and wrist surgery: a review. Art Int Surg. 2024;4:214-32.
10. Di Cosmo M, Fiorentino MC, Villani FP, et al. A deep learning approach to median nerve evaluation in ultrasound images of carpal tunnel inlet. Med Biol Eng Comput. 2022;60:3255-64.
11. Faeghi F, Ardakani AA, Acharya UR, et al. Accurate automated diagnosis of carpal tunnel syndrome using radiomics features with ultrasound images: a comparison with radiologists’ assessment. Eur J Radiol. 2021;136:109518.
12. Nakao K, Thavara BD, Tanaka R, et al. Surgeon experience of the surgical safety with KINEVO 900 in vascular neurosurgery: the initial experience. Asian J Neurosurg. 2020;15:464-7.
13. Lin KY, Li YT, Han JY, et al. Deep learning to detect triangular fibrocartilage complex injury in wrist MRI: retrospective study with internal and external validation. J Pers Med. 2022;12:1029.
14. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195.
15. Schweizer A, Mauler F, Vlachopoulos L, Nagy L, Fürnstahl P. Computer-assisted 3-dimensional reconstructions of scaphoid fractures and nonunions with and without the use of patient-specific guides: early clinical outcomes and postoperative assessments of reconstruction accuracy. J Hand Surg Am. 2016;41:59-69.
16. Wagner GA, Glennon A, Sieberer JM, Tommasini SM, Lattanza LL. A patient-specific three-dimensional-printed surgical guide for dorsal scaphoid fracture fixation: a comparative cadaver study. J Hand Surg Glob Online. 2025;7:158-66.
17. Wirth MA, Maniglio M, Jochum BC, et al. Three-dimensional-planned patient-specific guides for scaphoid reconstruction: a comparative study of primary and revision nonunion cases. J Clin Med. 2025;14:2082.
18. Ichikawa Y, Tobita M, Takahashi R, et al. Learning curve and ergonomics associated with the 3D-monitor-assisted microsurgery using a digital microscope. J Plast Reconstr Surg. 2023;2:1-8.
19. Rodriguez-Unda NA, Wu DS. Exoscope for upper extremity peripheral nerve surgery: revision carpal tunnel release with epineurolysis and hypothenar fat flap. Cureus. 2022;14:e22539.
20. Ernst J, Hahne JM, Markovic M, et al. Combining surgical innovations in amputation surgery-robotic harvest of the rectus abdominis muscle, transplantation and targeted muscle reinnervation improves myocontrol capability and pain in a transradial amputee. Medicina. 2023;59:2134.
21. Brauckmann V, Mayor JR, Ernst L, Ernst J. How a robotic visualization system can facilitate targeted muscle reinnervation. J Reconstr Microsurg Open. 2024;09:e19-26.
22. Besmens IS, Politikou O, Giovanoli P, Calcagni M, Lindenblatt N. Robotic microsurgery in extremity reconstruction - experience with a novel robotic system. Surg Innov. 2024;31:42-7.
23. Cannizzaro D, Scalise M, Zancanella C, Paulli S, Peron S, Stefini R. Comparative evaluation of major robotic systems in microanastomosis procedures: a systematic review of current capabilities and future potential. Brain Sci. 2024;14:1235.
24. van Mulken TJM, Schols RM, Scharmga AMJ, et al; MicroSurgical Robot Research Group. First-in-human robotic supermicrosurgery using a dedicated microsurgical robot for treating breast cancer-related lymphedema: a randomized pilot trial. Nat Commun. 2020;11:757.
25. van Mulken TJM, Wolfs JAGN, Qiu SS, et al; MicroSurgical Robot Research Group. One-year outcomes of the first human trial on robot-assisted lymphaticovenous anastomosis for breast cancer-related lymphedema. Plast Reconstr Surg. 2022;149:151-61.
26. Kueckelhaus M, Nistor A, van Mulken T, et al. Clinical experience in open robotic-assisted microsurgery: user consensus of the European Federation of Societies for Microsurgery. J Robot Surg. 2025;19:171.
27. Malzone G, Menichini G, Innocenti M, Ballestín A. Microsurgical robotic system enables the performance of microvascular anastomoses: a randomized in vivo preclinical trial. Sci Rep. 2023;13:14003.
28. van Boekel AM, van der Meijden SL, Arbous SM, et al. Systematic evaluation of machine learning models for postoperative surgical site infection prediction. PLoS One. 2024;19:e0312968.
29. Katsura Y, Ohga S, Shimo K, Hattori T, Yamada T, Matsubara T. A decision tree algorithm to identify predictors of post-stroke complex regional pain syndrome. Sci Rep. 2024;14:9893.
30. Wang AWT, Lefaivre KA, Potter J, et al. Complex regional pain syndrome after distal radius fracture: a survey of current practices. PLoS One. 2024;19:e0314307.
31. Bressler M, Merk J, Gohlke T, et al. A virtual reality serious game for the rehabilitation of hand and finger function: iterative development and suitability study. JMIR Serious Games. 2024;12:e54193.
32. Prahm C, Eckstein K, Bressler M, et al. PhantomAR: gamified mixed reality system for alleviating phantom limb pain in upper limb amputees-design, implementation, and clinical usability evaluation. J Neuroeng Rehabil. 2025;22:21.
33. AlShenaiber A, Datta S, Mosa AJ, Binhammer PA, Ing EB. Large language models in the diagnosis of hand and peripheral nerve injuries: an evaluation of ChatGPT and the isabel differential diagnosis generator. J Hand Surg Glob Online. 2024;6:847-54.
34. Pressman SM, Borna S, Gomez-Cabello CA, Haider SA, Forte AJ. AI in hand surgery: assessing large language models in the classification and management of hand injuries. J Clin Med. 2024;13:2832.
35. Demir KC, Schieber H, Weise T, et al. Deep learning in surgical workflow analysis: a review of phase and step recognition. IEEE J Biomed Health Inform. 2023;27:5405-17.
36. Perkins SW, Muste JC, Alam T, Singh RP. Improving clinical documentation with artificial intelligence: a systematic review. Perspect Health Inf Manag. 2024;21:1d.
37. Khanna A, Wolf T, Frank I, et al. Enhancing accuracy of operative reports with automated artificial intelligence analysis of surgical video. J Am Coll Surg. 2025;240:739-46.
38. Wyles CC, Fu S, Odum SL, et al. External validation of natural language processing algorithms to extract common data elements in THA operative notes. J Arthroplasty. 2023;38:2081-4.
39. Harvey CJ, Wong V, Huynh W, Lee JP, Woo RK. Ambient AI-assited clinical documentation in surgical outpatient care: a preliminary study of usability, workflow, and patient experience. World J Pediatr Surg. 2025;8:e001073.
40. Will J, Gupta M, Zaretsky J, Dowlath A, Testa P, et al. Enhancing the readability of online patient education materials using large language models: cross-sectional study. J Med Internet Res. 2025;27:e69955.
41. Ararat K, Altan O, Serbest S, Baser O, Dumanli S. A biodegradable implant antenna detecting post-surgical infection. In: Proceedings of the 14th European Conference on Antennas and Propagation (EuCAP); 2020 Mar 15-20; Copenhagen, Denmark. IEEE; 2020. pp. 1-4.
42. Arifuzzaman M, Millhouse P, Raval Y, et al. An implanted pH sensor read using radiography. Analyst. 2019;144:2984-93.
43. Yangi K, On TJ, Xu Y, et al. Artificial intelligence integration in surgery through hand and instrument tracking: a systematic literature review. Front Surg. 2025;12:1528362.
44. Li CR, Chang YJ, Lin MS, Tsou HK. Augmented reality in spine surgery: a case study of atlantoaxial instrumentation in Os odontoideum. Medicina. 2024;60:874.
45. Atay S. Interpretable machine learning in healthcare: comparison and evaluation of interpretable models. Bachelor Thesis, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 2022. Available from: https://www.researchgate.net/publication/393449289_Interpretable_Machine_Learning_in_Healthcare_Comparison_and_Evaluation_of_Interpretable_Models. [Last accessed on 8 Jan 2026].
46. Yadav N, Pandey S, Gupta A, Dudani P, Gupta S, Rangarajan K. Data privacy in healthcare: in the era of artificial intelligence. Indian Dermatol Online J. 2023;14:788-92.
47. Regulation GDP. General data protection regulation (GDPR) - official legal text. Gen Data Prot Regul. 2016.
48. States. Health Insurance Portability and Accountability Act of 1996. Public Law 104-191. US Statut Large. 1996;110:1936-2103.
49. Neural Trust. AI in healthcare: protecting patient data. Available from: https://neuraltrust.ai/blog/ai-healthcare-protecting-patient-data. [Last accessed on 29 Dec 2025].
50. Duffourc M, Møllebæk M, Druedahl LC, Minssen T, Gerke S. Surgeons’ perspectives on liability for the use of artificial intelligence technologies in the United States and European Union: results from a focus group study. Ann Surg Open. 2025;6:e542.
51. See B. Paging doctor robot: medical artificial intelligence, tort liability, and why personhood may be the answer. Brook L Rev. 2021;87:417-56. Available from: https://brooklynworks.brooklaw.edu/blr/vol87/iss1/10/. [Last accessed on 8 Jan 2026].
