REFERENCES
1. Gillies SHD, Millard DR. Principles and art of plastic surgery. Available from: https://archive.org/details/principlesartofp0000gill/page/n7/mode/2up. [Last accessed on 28 Feb 2026].
2. Ruccia F, Mavilakandy A, Imtiaz H, et al. The application of robotics in plastic and reconstructive surgery: a systematic review. Int J Med Robot. 2024;20:e2661.
3. Tan YPA, Liverneaux P, Wong JKF. Current limitations of surgical robotics in reconstructive plastic microsurgery. Front Surg. 2018;5:22.
4. Innocenti M, Malzone G, Menichini G. First-in-human free flap tissue reconstruction using a dedicated microsurgical robotic platform. Plast Reconstr Surg. 2023;151:1078-82.
5. 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.
6. 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.
7. Yu E, Chu X, Zhang W, et al. Large language models in medicine: applications, challenges, and future directions. Int J Med Sci. 2025;22:2792-801.
8. Farid Y, Fernando Botero Gutierrez L, Ortiz S, et al. Artificial intelligence in plastic surgery: insights from plastic surgeons, education integration, chatgpt’s survey predictions, and the path forward. Plast Reconstr Surg Glob Open. 2024;12:e5515.
9. Mansoor M, Ibrahim AF. The transformative role of artificial intelligence in plastic and reconstructive surgery: challenges and opportunities. J Clin Med. 2025;14:2698.
10. Hassan AM, Biaggi AP, Asaad M, et al. Development and assessment of machine learning models for individualized risk assessment of mastectomy skin flap necrosis. Ann Surg. 2023;278:e123-30.
11. Tighe D, McMahon J, Schilling C, Ho M, Provost S, Freitas A. Machine learning methods applied to risk adjustment of cumulative sum chart methodology to audit free flap outcomes after head and neck surgery. Br J Oral Maxillofac Surg. 2022;60:1353-61.
12. Kapila AK, Georgiou L, Hamdi M. Decoding the impact of AI on microsurgery: systematic review and classification of six subdomains for future development. Plast Reconstr Surg Glob Open. 2024;12:e6323.
13. Park BJ, Hunt SJ, Martin C 3rd, Nadolski GJ, Wood BJ, Gade TP. Augmented and mixed reality: technologies for enhancing the future of IR. J Vasc Interv Radiol. 2020;31:1074-82.
14. Duran A, Demiröz A, Çörtük O, Ok B, Özten M, Eroğlu S. Human vs machine: the future of decision-making in plastic and reconstructive surgery. Aesthet Surg J. 2025;45:434-40.
15. Huang H, Lu Wang M, Chen Y, Chadab TM, Vernice NA, Otterburn DM. A machine learning approach to predicting donor site complications following DIEP flap harvest. J Reconstr Microsurg. 2024;40:70-7.
16. Kiwan O, Al-Kalbani M, Rafie A, Hijazi Y. Artificial intelligence in plastic surgery, where do we stand? JPRAS Open. 2024;42:234-43.
17. Park KW, Diop M, Willens SH, Pepper JP. Artificial intelligence in facial plastics and reconstructive surgery. Otolaryngol Clin North Am. 2024;57:843-52.
18. von Reibnitz D, Weinzierl A, Barbon C, et al. 100 anastomoses: a two-year single-center experience with robotic-assisted micro- and supermicrosurgery for lymphatic reconstruction. J Robot Surg. 2024;18:164.
19. 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.
20. Rusch M, Hoffmann G, Wieker H, et al. Evaluation of the MMI Symani(®) robotic microsurgical system for coronary-bypass anastomoses in a cadaveric porcine model. J Robot Surg. 2024;18:168.
21. Vles MD, Terng NCO, Zijlstra K, Mureau MAM, Corten EML. Virtual and augmented reality for preoperative planning in plastic surgical procedures: a systematic review. J Plast Reconstr Aesthet Surg. 2020;73:1951-9.
22. Kim Y, Kim H, Kim YO. Virtual reality and augmented reality in plastic surgery: a review. Arch Plast Surg. 2017;44:179-87.
23. Sayadi LR, Naides A, Eng M, et al. The new frontier: a review of augmented reality and virtual reality in plastic surgery. Aesthet Surg J. 2019;39:1007-16.
24. McGraw JR, Wakim JJ, Gallagher RS, Kovach SJ 3rd. Intraoperative navigation in plastic surgery with augmented reality: a preclinical validation study. Plast Reconstr Surg. 2023;151:170e-1.
25. Cai EZ, Gao Y, Ngiam KY, Lim TC. Mixed reality intraoperative navigation in craniomaxillofacial surgery. Plast Reconstr Surg. 2021;148:686e-8.
26. Shafarenko MS, Catapano J, Hofer SOP, Murphy BD. The role of augmented reality in the next phase of surgical education. Plast Reconstr Surg Glob Open. 2022;10:e4656.
27. Kowalewski KF, Hendrie JD, Schmidt MW, et al. Validation of the mobile serious game application Touch SurgeryTM for cognitive training and assessment of laparoscopic cholecystectomy. Surg Endosc. 2017;31:4058-66.
28. Kyaw BM, Saxena N, Posadzki P, et al. Virtual reality for health professions education: systematic review and meta-analysis by the digital health education collaboration. J Med Internet Res. 2019;21:e12959.
29. Patel I, Om A, Cuzzone D, Garcia Nores G. Comparing ChatGPT vs surgeon-generated informed consent documentation for plastic surgery procedures. Aesthet Surg J Open Forum. 2024;6:ojae092.
30. Mess SA, Mackey AJ, Yarowsky DE. Artificial intelligence scribe and large language model technology in healthcare documentation: advantages, limitations, and recommendations. Plast Reconstr Surg Glob Open. 2025;13:e6450.
31. Ong CS, Obey NT, Zheng Y, Cohan A, Schneider EB. SurgeryLLM: a retrieval-augmented generation large language model framework for surgical decision support and workflow enhancement. NPJ Digit Med. 2024;7:364.
32. Genovese A, Prabha S, Borna S, et al. Artificial intelligence for patient support: assessing retrieval-augmented generation for answering postoperative rhinoplasty questions. Aesthet Surg J. 2025;45:735-44.
33. Song T, Pabst F, Eck U, Navab N. Enhancing patient acceptance of robotic ultrasound through conversational virtual agent and immersive visualizations. arXiv 2025; arXiv:2502.10088.
34. Byrd Iv TF, Tignanelli CJ. Artificial intelligence in surgery - a narrative review. J Med Artif Intell. 2024;7:29.
35. Pressman SM, Borna S, Gomez-Cabello CA, Haider SA, Haider C, Forte AJ. AI and ethics: a systematic review of the ethical considerations of large language model use in surgery research. Healthcare. 2024;12:825.
36. Gilbert S. The EU passes the AI Act and its implications for digital medicine are unclear. NPJ Digit Med. 2024;7:135.
37. Vokinger KN, Gasser U. Regulating AI in medicine in the United States and Europe. Nat Mach Intell. 2021;3:738-9.
38. Warraich HJ, Tazbaz T, Califf RM. FDA perspective on the regulation of artificial intelligence in health care and biomedicine. JAMA. 2025;333:241-7.
39. Oakden-Rayner L, Dunnmon J, Carneiro G, Ré C. Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. Proc ACM Conf Health Inference Learn. 2020;2020:151-9.
40. Johnson AE, Ghassemi MM, Nemati S, Niehaus KE, Clifton DA, Clifford GD. Machine learning and decision support in critical care. Proc IEEE Inst Electr Electron Eng. 2016;104:444-66.
41. Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. A survey on bias and fairness in machine learning. ACM Comput Surv. 2021;54:1-35.
42. Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet. 2019;393:1577-9.
43. Gu H, Liang Y, Xu Y, et al. Improving workflow integration with xPath: design and evaluation of a human-AI diagnosis system in pathology. ACM Trans Comput Hum Interact. 2023;30:1-37.
44. Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020;26:1364-74.
45. WIRED. AI can help patients - but only if doctors understand it. Available from: https://www.wired.com/story/ai-help-patients-doctors-understand/. [Last accessed on 28 Feb 2026].
46. Bruynseels K, Santoni de Sio F, van den Hoven J. Digital twins in health care: ethical implications of an emerging engineering paradigm. Front Genet. 2018;9:31.
47. Parvin N, Joo SW, Jung JH, Mandal TK. Multimodal AI in biomedicine: pioneering the future of biomaterials, diagnostics, and personalized healthcare. Nanomaterials. 2025;15:895.
48. Jarvis T, Thornburg D, Rebecca AM, Teven CM. Artificial intelligence in plastic surgery: current applications, future directions, and ethical implications. Plast Reconstr Surg Glob Open. 2020;8:e3200.
