Artificial intelligence in aesthetic and reconstructive surgery: Clinical applications, ethical challenges, and future trends

Artificial intelligence (AI) and machine learning (ML) are increasingly influencing aesthetic and reconstructive surgery. These technologies are transforming clinical workflows by enhancing precision, personalization, and operational efficiency across various stages of surgical care. Aim: To review the current applications, measurable benefits, and challenges of AI and ML in aesthetic and reconstructive surgery, and to explore their potential future impact on the field.

Material and methods. This review synthesizes findings from recent studies, technological assessments, and clinical applications of AI and ML in surgical practice. Key areas examined include preoperative planning, imaging, robotic systems, intraoperative tools, and postoperative monitoring.

Results. AI and ML have been shown to reduce surgical planning time by up to 35 % and improve breast symmetry assessment accuracy by over 90 %. Robotic systems and AI-powered automation enhance minimally invasive procedures and optimize intraoperative decisions. Furthermore, AI supports postoperative care through predictive modeling, complication monitoring, and real-time data interpretation. Despite these advances, challenges persist, including algorithmic bias, data privacy concerns, and the need for robust clinical validation.

Conclusions. AI and ML are poised to significantly reshape aesthetic and reconstructive surgery. As these technologies continue to evolve, addressing ethical and regulatory challenges will be essential for their safe and effective integration into clinical practice.

1. Karalis V.D. The integration of artificial intelligence into clinical practice. Applied Biosciences. 2024;3(1):14–44. doi: 10.3390/applbiosci3010002

2. Maleki Varnosfaderani S., Forouzanfar M. The role of AI in hospitals and clinics: transforming healthcare in the 21st century. Bioengineering (Basel). 2024;11(4):337. doi: 10.3390/bioengineering11040337

3. van Duong T., Vy V.P.T., Hung T.N.K. Artificial intelligence in plastic surgery: advancements, applications, and future. Cosmetics. 2024;11(4):109. doi: 10.3390/cosmetics11040109

4. Busch F., Hoffmann L., Rueger C., van Dijk E.H., Kader R., Ortiz-Prado E., Makowski M.R., Saba L., Hadamitzky M., Kather J.N., … Bressem K.K. Current applications and challenges in large language models for patient care: a systematic review. Commun. Med. (Lond). 2025;5(1). doi: 10.1038/s43856-024-00717-2

5. Dixon D., Sattar H., Moros N., Kesireddy S.R., Ahsan H., Lakkimsetti M., Fatima M., Doshi D., Sadhu K., Junaid Hassan M. Unveiling the influence of AI predictive analytics on patient outcomes: a comprehensive narrative review. Cureus. 2024;16(5):e59954. doi: 10.7759/cureus.59954

6. Dhawan R., Shauly O., Shay D., Brooks K., Losken A. Growth in FDA-approved artificial intelligence devices in plastic surgery: a key look into the future. Aesthetic Surg. J. 2024;45(1):108–111. doi: 10.1093/asj/sjae209

7. Barone M., de Bernardis R., Persichetti P. Artificial intelligence in plastic surgery: analysis of applications, perspectives, and psychological impact. Aesthetic Plast. Surg. 2025;49(5):1637–1639. doi: 10.1007/s00266-024-03988-1

8. Fortune-Ely M., Achanta M., Song M.S. The future of artificial intelligence in facial plastic surgery. JPRAS Open. 2023;39:89–92. doi: 10.1016/j.jpra.2023.11.016

9. Olejnik A., Verstraete L., Croonenborghs T.M., Politis C., Swennen G.R.J. The accuracy of three-dimensional soft tissue simulation in orthognathic surgery – a systematic review. J. Imaging. 2024;10(5):119. doi: 10.3390/jimaging10050119

10. Dong F., Yan J., Zhang X., Zhang Y., Liu D., Pan X., Xue L., Liu Y. Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery. Heliyon. 2024;10(15):e35742. doi: 10.1016/j.heliyon.2024.e35742

11. Hassan A.M., Rajesh A., Asaad M., Nelson J.A., Coert J.H., Mehrara B.J., Butler C.E. Artificial intelligence and machine learning in prediction of surgical complications: current state, applications, and implications. Am. Surg. 2023;89(1):25–30. doi: 10.1177/00031348221101488

12. Khalifa M., Albadawy M. Artificial intelligence for clinical prediction: exploring key domains and essential functions. Computer Methods and Programs in Biomedicine Update. 2024;5:100148. doi: 10.1016/j.cmpbup.2024.100148

13. Basile F.V., Oliveira T.S. Using machine learning to select breast implant volume. Plast. Reconstr. Surg. 2024;154(3):470e–477e. doi: 10.1097/PRS.0000000000011146

14. Jain Y., Lanjewar R., Shinde R.K. Revolutionising breast surgery: a comprehensive review of robotic innovations in breast surgery and reconstruction. Cureus. 2024;16(1):e52695. doi: 10.7759/cureus.52695

15. Boczar D., Sisti A., Oliver J.D., Helmi H., Restrepo D.J., Huayllani M.T., Spaulding A.C., Carter R., Rinker B.D., Forte A.J. Artificial intelligent virtual assistant for plastic surgery patient’s frequently asked questions. Ann. Plast. Surg. 2020;84(4):e16–e21. doi: 10.1097/sap.0000000000002252

16. Ryan M.L., Wang S., Pandya S.R. Integrating artificial intelligence into the visualization and modeling of three-dimensional anatomy in pediatric surgical patients. J. Pediatr. Surg. 2024;59(12):161629. doi: 10.1016/j.jpedsurg.2024.07.014

17. Alam M.K., Alftaikhah S.A., Issrani R., Ronsivalle V., Lo Giudice A., Cicciù M., Minervini G. Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: a systematic review and meta-analysis of in-vitro studies. Heliyon. 2024;10(3):e24221. doi: 10.1016/j.heliyon.2024.e24221

18. Juhnke B., Mattson A.R., Saltzman D., Azakie A., Hoggard E., Ambrose M., Iaizzo P.A., Erdman A., Fischer G. Use of virtual reality for pre-surgical planning in separation of conjoined twins: a case report. Proc. Inst. Mech. Eng. H. 2019;233(12):1327–1332. doi: 10.1177/0954411919878067

19. Riddle E.W., Kewalramani D., Narayan M., Jones D.B. Surgical simulation: virtual reality to artificial intelligence. Curr. Probl. Surg. 2024;61(11):101625. doi: 10.1016/j.cpsurg.2024.101625

20. Long Y., Cao J., Deguet A., Taylor R.H., Dou Q. Integrating artificial intelligence and augmented reality in robotic surgery: an initial DVRK study using a surgical education scenario. arXiv. 2022. doi: 10.48550/arxiv.2201.00383

21. Iftikhar M., Saqib M., Zareen M., Mumtaz H. Artificial intelligence: revolutionizing robotic surgery: review. Ann. Med. Surg. (Lond). 2024;86(9):5401– 5409. doi: 10.1097/MS9.0000000000002426

22. Chinski H., Lerch R., Tournour D., Chinski L., Caruso D. An artificial intelligence tool for image simulation in rhinoplasty. Facial Plast. Surg. 2021;38(2):201–206. doi: 10.1055/s-0041-1729911

23. Alowais S.A., Alghamdi S.S., Alsuhebany N., Alqahtani T., Alshaya A.I., Almohareb S.N., Aldairem A., Alrashed M., Bin Saleh K., Badreldin H.A., … Albekairy A.M. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med. Educ. 2023;23(1):689. doi: 10.1186/s12909-023-04698-z

24. Park J.J., Tiefenbach J., Demetriades A.K. The role of artificial intelligence in surgical simulation. Front. Med. Technol. 2022;4:1076755. doi: 10.3389/ fmedt.2022.1076755

25. Hla D.A., Hindin D.I. Generative AI and machine learning in surgical education. Curr. Probl. Surg. 2025:63:101701. doi: 10.1016/j.cpsurg.2024.101701

26. Leivaditis V., Beltsios E., Papatriantafyllou A., Grapatsas K., Mulita F., Kontodimopoulos N., Baikoussis N.G., Tchabashvili L., Tasios K., Maroulis I., Dahm M., Koletsis E. Artificial intelligence in cardiac surgery: transforming outcomes and shaping the future. Clin. Pract. 2025;15(1):17. doi: 10.3390/clinpract15010017

27. Cowan R., Mann G., Salibian A.A. Ultrasound in microsurgery: current applications and new frontiers. J. Clin. Med. 2024;13(12):3412. doi: 10.3390/jcm13123412

28. Bellini V., Valente M., Bertorelli G., Pifferi B., Craca M., Mordonini M., Lombardo G., Bottani E., Del Rio P., Bignami E. Machine learning in perioperative medicine: a systematic review. J. Anesth. Analg. Crit. Care. 2022;2(1):2. doi: 10.1186/s44158-022-00033-y

29. Rashid A., Feinberg L., Fan K. The application of cone beam computed tomography (CBCT) onthe diagnosis and management of maxillofacial trauma. Diagnostics (Basel). 2024;14(4):373. doi: 10.3390/diagnostics14040373

30. Chen M.Y., Cao M.Q., Xu T.Y. Progress in the application of artificial intelligence in skin wound assessment and prediction of healing time. Am. J. Transl. Res. 2024;16(7):2765–2776. doi: 10.62347/MYHE3488

31. Moura F.S., Amin K., Ekwobi C. Artificial intelligence in the management and treatment of burns: a systematic review. Burns Trauma. 2021;9:tkab022. doi: 10.1093/burnst/tkab022

32. Dutta P., Upadhyay P., De M., Khalkar R. Medical image analysis using deep convolutional neural networks: CNN architectures and transfer learning. 2022 International Conference on Inventive Computation Technologies (ICICT). 2020:175–180. doi: 10.1109/icict48043.2020.9112469

33. Braza M.E., Marietta M., Fahrenkopf M.P. Split-thickness skin grafts. In: StatPearls [Internet]. StatPearls Publishing; 2023. Available at: https://www.ncbi.nlm.nih.gov/books/NBK551561/

34. Shakir A., Chang D.W. Advances in deep inferior epigastric perforator flap breast reconstruction. Annals of Breast Surgery. 2020;4:26. doi: 10.21037/abs-20-87

35. Srinivas S., Young A.J. Machine learning and artificial intelligence in surgical research. Surg. Clin. North Am. 2023;103(2):299–316. doi: 10.1016/j. suc.2022.11.002

36. Yassa A., Akhavan A., Ayad S., Ayad O., Colon A., Ignatiuk A. The surgeon’s digital eye: assessing artificial intelligence–generated images in breast augmentation and reduction. Plast. Reconstr. Surg. Glob. Open. 2024;12(12):e6295. doi: 10.1097/gox.0000000000006295

37. Thamm O.C., Eschborn J., Schäfer R.C., Schmidt J. Advances in modern microsurgery. J. Clin. Med. 2024;13(17):5284. doi: 10.3390/jcm13175284

38. Danciu R., Danciu B.A., Vasiu L., Avino A., Filip C.I., Hariga C., Răducu L., Jecan R.C. Deep learning-based flap detection system using thermographic images in plastic surgery. Applied System Innovation. 2024;7(6):101. doi: 10.3390/asi7060101

39. Marchesi A., Garieri P., Amendola F., Marcelli S., Vaienti L. Intraoperative near-infrared spectroscopy for pedicled perforator flaps: a possible tool for the early detection of vascular issues. Arch. Plast. Surg. 2021;48(4):457–461. doi: 10.5999/aps.2019.00311

40. Kohlert S., Quimby A.E., Saman M., Ducic Y. Postoperative free-flap monitoring techniques. Semin. Plast. Surg. 2019;33(1):13–16. doi: 10.1055/s-0039-1677880

41. Shajari S., Kuruvinashetti K., Komeili A., Sundararaj U. The emergence of AI-based wearable sensors for digital health technology: a review. Sensors (Basel). 2023;23(23):9498. doi: 10.3390/s23239498

42. Knoedler S., Hoch C.C., Huelsboemer L., Knoedler L., Stögner V.A., Pomahac B., Kauke-Navarro M., Colen D. Postoperative free flap monitoring in reconstructive surgery – man or machine? Front. Surg. 2023;10:1130566. doi: 10.3389/fsurg.2023.1130566

43. Xu J., Anastasiou D., Booker J., Burton O.E., Layard Horsfall H., Salvadores Fernandez C., Xue Y., Stoyanov D., Tiwari M.K., Marcus H.J., Mazomenos E.B. A deep learning approach to classify surgical skill in microsurgery using force data from a novel sensorised surgical glove. Sensors (Basel). 2023;23(21):8947. doi: 10.3390/s23218947

44. Shahrezaei A., Sohani M., Taherkhani S., Zarghami S.Y. The impact of surgical simulation and training technologies on general surgery education. BMC Med. Educ. 2024;24(1):1297. doi: 10.1186/s12909-024-06299-w

45. Bugdadi A., Sawaya R., Bajunaid K., Olwi D., Winkler-Schwartz A., Ledwos N., Marwa I., Alsideiri G., Sabbagh A.J., Alotaibi F.E., Al-Zhrani G., Maestro R.D. Is virtual reality surgical performance influenced by force feedback device utilized? J. Surg. Educ. 2018;76(1):262–273. doi: 10.1016/j.jsurg.2018.06.012

46. Haykal D. Emerging and pioneering AI technologies in aesthetic dermatology: sketching a path toward personalized, predictive, and proactive care. Cosmetics. 2024;11(6):206. doi: 10.3390/cosmetics11060206

47. Vatiwutipong P., Vachmanus S., Noraset T., Tuarob S. Artificial intelligence in cosmetic dermatology: a systematic literature review. IEEE Access. 2023;11:71407–71425. doi: 10.1109/access.2023.3295001

48. Haykal D. Harnessing AI in laser aesthetic treatments: revolutionizing precision, safety, and personalization. J. Cosmet. Dermatol. 2025;24(2):e16704. doi: 10.1111/jocd.16704

49. Liao J., Li X., Gan Y., Han S., Rong P., Wang W., Li W., Zhou L. Artificial intelligence assists precision medicine in cancer treatment. Front. Oncol. 2023;12:998222. doi: 10.3389/fonc.2022.998222

50. Mulholland R.S. Radio frequency energy for non-invasive and minimally invasive skin tightening. Clin. Plast. Surg. 2011;38(3):437–448. doi: 10.1016/j.cps.2011.05.003

51. Shome D., Vadera S., Ram M.S., Khare S., Kapoor R. Use of micro-focused ultrasound for skin tightening of mid and lower face. Plast. Reconstr. Surg. Glob. Open. 2019;7(12):e2498. doi: 10.1097/GOX.0000000000002498

52. Thunga S., Khan M., Cho S.I., Na J.I., Yoo J. AI in aesthetic/cosmetic dermatology: current and future. J. Cosmet. Dermatol. 2025;24(1):e16640. doi: 10.1111/jocd.16640

53. Johnson K.B., Wei W.Q., Weeraratne D., Frisse M.E., Misulis K., Rhee K., Zhao J., Snowdon J.L. Precision medicine, AI, and the future of personalized health care. Clin. Transl. Sci. 2021;14(1):86–93. doi: 10.1111/cts.12884

54. Li Z., Koban K.C., Schenck T.L., Giunta R.E., Li Q., Sun Y. Artificial intelligence in dermatology image analysis: current developments and future trends. J. Clin. Med. 2022;11(22):6826. doi: 10.3390/jcm11226826

55. Taeger J., Bischoff S., Hagen R., Rak K. Utilization of smartphone depth mapping cameras for appbased grading of facial movement disorders: development and feasibility study. JMIR Mhealth. Uhealth. 2021;9(1):e19346. doi: 10.2196/19346

56. Ali A., Shaukat H., Bibi S., Altabey W.A., Noori M., Kouritem S.A. Recent progress in energy harvesting systems for wearable technology. Energy Strategy Reviews. 2023;49:101124. doi: 10.1016/j.esr.2023.101124

57. Frank K., Day D., Few J., Chiranjiv C., Gold M., Sattler S., Kerscher M., Knoedler L., Filippo A., Rzany B., … Huang P. AI assistance in aesthetic medicine – a consensus on objective medical standards. J. Cosmet. Dermatol. 2024;23(12):4110–4115. doi: 10.1111/jocd.16481

58. Kapoor K.M., Kapoor A., Bertossi D. Role of robotics in neuromodulator and filler injections of face. Indian J. Plast. Surg. 2023;56(5):470–473. doi: 10.1055/s-0043-1775867

59. Reddy K., Gharde P., Tayade H., Patil M., Reddy L.S., Surya D. Advancements in robotic surgery: a comprehensive overview of current utilizations and upcoming frontiers. Cureus. 2023;15(12):e50415. doi: 10.7759/cureus.50415

60. Arienzo V.P., Goldenberg D.C., Noronha M.A.N., Lucas P.F.S., Ferreira B.P.V., Oliveira T.S. Robotic and plastic surgery: actuality and prospects for the near future, a scoping review. Einstein (Sao Paulo). 2024;22:eRW0710. doi: 10.31744/einstein_journal/2024RW0710

61. Gorgy A., Xu H.H., Hawary H.E., Nepon H., Lee J., Vorstenbosch J. Integrating AI into breast reconstruction surgery: exploring opportunities, applications, and challenges. Plast. Surg. (Oakv). 2024:22925503241292349. doi: 10.1177/22925503241292349

62. Takeuchi M., Kitagawa Y. Artificial intelligence and surgery. Ann. Gastroenterol. Surg. 2023;8(1):4–5. doi: 10.1002/ags3.12766

63. Shetti A.N., Ingale P.C., Mavi S., Chaudhari S.P., Doshi S.S. The role of artificial intelligence in enhancing surgical precision and outcomes. IP Journal of Surgery and Allied Sciences. 2024;6(3):78–81. doi: 10.18231/j.jsas.2024.017

64. Handa A., Gaidhane A., Choudhari S.G. Role of robotic-assisted surgery in public health: its advantages and challenges. Cureus. 2024;16(6):e62958. doi: 10.7759/cureus.62958

65. Iacob E.R., Iacob R., Ghenciu L.A., Popoiu T.A., Stoicescu E.R., Popoiu C.M. Small scale, high precision: robotic surgery in neonatal and pediatric patients – a narrative review. Children (Basel). 2024;11(3):270. doi: 10.3390/children11030270

66. Imran H., Shuja M.H., Abid M., Khemane Z., Haque M.A., Abbasi A.F. Robotic surgery: augmenting surgeons’ skills or replacing them? International Journal of Surgery Global Health. 2024;7(6). doi: 10.1097/gh9.0000000000000515

67. Adegbesan A., Akingbola A., Aremu O., Adewole O., Amamdikwa J.C., Shagaya U. From scalpels to algorithms: the risk of dependence on artificial intelligence in surgery. Journal of Medicine Surgery and Public Health. 2024;100140. doi: 10.1016/j.glmedi.2024.100140

68. Cascini F., Santaroni F., Lanzetti R., Failla G., Gentili A., Ricciardi W. Developing a data-driven approach in order to improve the safety and quality of patient care. Front. Public Health. 2021;9:667819. doi: 10.3389/fpubh.2021.667819

69. Al-Raeei M. The future of oral oncology: how artificial intelligence is redefining surgical procedures and patient management. Int. Dent. J. 2025;75(1):109– 116. doi: 10.1016/j.identj.2024.09.032

70. Callahan A., Fries J.A., Ré C., Huddleston J.I., Giori N.J., Delp S., Shah N.H. Medical device surveillance with electronic health records. NPJ Digit. Med. 2019;2:94. doi: 10.1038/s41746-019-0168-z

71. Seth I., Bulloch G., Joseph K., Hunter-Smith D.J., Rozen W.M. Use of artificial intelligence in the advancement of breast surgery and implications for breast reconstruction: a narrative review. J. Clin. Med. 2023;12(15):5143. doi: 10.3390/jcm12155143

72. Hassan A.M., Biaggi-Ondina A., Asaad M., Morris N., Liu J., Selber J.C., Butler C.E. Artificial intelligence modeling to predict periprosthetic infection and explantation following implant-based reconstruction. Plast. Reconstr. Surg. 2023;152(5):929–938. doi: 10.1097/prs.0000000000010345

73. Merath K., Hyer J.M., Mehta R., Farooq A., Bagante F., Sahara K., Tsilimigras D.I., Beal E., Paredes A.Z., Wu L., Ejaz A., Pawlik T.M. Use of machine learning for prediction of patient risk of postoperative complications after liver, pancreatic, and colorectal surgery. J. Gastrointest. Surg. 2019;24(8):1843–1851. doi: 10.1007/s11605-019-04338-2

74. Jeddi Z., Bohr A. Remote patient monitoring using artificial intelligence. In: Artificial intelligence in healthcare. Elsevier; 2020. P. 203–234. doi: 10.1016/b978-0-12-818438-7.00009-5

75. Serrano L.P., Maita K.C., Avila F.R., TorresGuzman R.A., Garcia J.P., Eldaly A.S., Haider C.R., Felton C.L., Paulson M.R., Maniaci M.J., Forte A.J. Benefits and challenges of remote patient monitoring as perceived by health care practitioners: a systematic review. Perm. J. 2023;27(4):100–111. doi: 10.7812/TPP/23.022

76. Marey A., Arjmand P., Alerab A.D.S., Eslami M.J., Saad A.M., Sanchez N., Umair M. Explainability, transparency and black box challenges of AI in radiology: impact on patient care in cardiovascular radiology. Egyptian Journal of Radiology and Nuclear Medicine. 2024;55:183. doi: 10.1186/s43055-024-01356-2

77. Ren Y., Tripathi C., Guan Z., Zhu R., Hougha V., Ma Y., Hu Z., Balch J., Loftus T.J., Rashidi P., … Bihorac A. Transparent AI: developing an explainable interface for predicting postoperative complications. arXiv. 2024;2404.16064. doi: 10.48550/arxiv.2404.16064

78. Singh A., Seth I., Lim B., Cuomo R., Rozen W.M. Ethical issues of artificial intelligence in plastic surgery: a narrative review. Plastic and Aesthetic Research. 2024. doi: 10.20517/2347-9264.2024.108

79. Cross J.L., Choma M.A., Onofrey J.A. Bias in medical AI: implications for clinical decision-making. PLOS Digital Health. 2024;3(11):e0000651. doi: 10.1371/journal.pdig.0000651

80. Hanna M.G., Pantanowitz L., Jackson B., Palmer O., Visweswaran S., Pantanowitz J., Deebajah M., Rashidi H.H. Ethical and bias considerations in artificial intelligence (AI)/machine learning. Mod. Pathol. 2024;38(3):100686. doi: 10.1016/j.modpat.2024.100686

81. Chen Y., Clayton E.W., Novak L.L., Anders S., Malin B. Human-centered design to address biases in artificial intelligence. J .Med. Internet Res. 2023;25:e43251. doi: 10.2196/43251

82. Farhud D.D., Zokaei S. Ethical issues of artificial intelligence in medicine and healthcare. Iran J. Public Health. 2021;50(11):i–v. doi: 10.18502/ijph.v50i11.7600

83. Santoso W., Safitri R., Samidi S. Integration of artificial intelligence in facial recognition systems for software security. SinkrOn. 2024;8(2):1208–1214. doi: 10.33395/sinkron.v8i2.13612

84. Shojaei P., Vlahu-Gjorgievska E., Chow Y. Security and privacy of technologies in health information systems: a systematic literature review. Computers. 2024;13(2):41. doi: 10.3390/computers13020041

85. Rokhshad R., Keyhan S.O., Yousefi P. Artificial intelligence applications and ethical challenges in oral and maxillo-facial cosmetic surgery: a narrative review. Maxillofac. Plast. Reconstr. Surg. 2023;45(1):14. doi: 10.1186/s40902-023-00382-w

86. Loftus T.J., Tighe P.J., Filiberto A.C., Efron P.A., Brakenridge S.C., Mohr A.M, Rashidi P., Upchurch G.R. Jr., Bihorac A. Artificial intelligence and surgical decision-making. JAMA Surgery. 2020;155(2):148–158. doi: 10.1001/jamasurg.2019.4917

87. Morris M.X., Song E.Y., Rajesh A., Asaad M., Phillips B.T. Ethical, legal, and financial considerations of artificial intelligence in surgery. Am. Surg. 2023;89(1):55–60. doi: 10.1177/00031348221117042

88. Gurevich E., Hassan B.E., Morr C.E. Equity within AI systems: what can health leaders expect? Healthc. Manage. Forum. 2022;36(2):119–124. doi: 10.1177/08404704221125368

89. Mir M.A. Artificial intelligence revolutionizing plastic surgery scientific publications. Cureus. 2023;15(6):e40770. doi: 10.7759/cureus.40770

90. Rao L., Yang E., Dissanayake S., Cuomo R., Seth I., Rozen W.M. The use of generative artificial intelligence in surgical education: a narrative review. Plastic and Aesthetic Research. 2024. doi: 10.20517/2347-9264.2024.102

91. Morris M.X., Fiocco D., Caneva T., Yiapanis P., Orgill D.P. Current and future applications of artificial intelligence in surgery: implications for clinical practice and research. Front. Surg. 2024;11:1393898. doi: 10.3389/fsurg.2024.1393898

92. Hamilton A. The future of artificial intelligence in surgery. Cureus. 2024;16(7):e63699. doi: 10.7759/cureus.63699

93. Parekh A.E., Shaikh O.A., Simran, Manan S., Hasibuzzaman M.A. Artificial intelligence (AI) in personalized medicine: AI-generated personalized therapy regimens based on genetic and medical history: short communication. Ann. Med. Surg. (Lond). 2023;85(11):5831–5833. doi: 10.1097/MS9.0000000000001320

94. Olawade D.B., Marinze S., Qureshi N., Weerasinghe K., Teke J. The impact of artificial intelligence and machine learning in organ retrieval and transplantation: a comprehensive review. Curr. Res. Transl. Med. 2025;73(2):103493. doi: 10.1016/j.retram.2025.103493

95. Rodler S., Ganjavi C., de Backer P., Magoulianitis V.., Ramacciotti L.S., de Castro Abreu A.L., Gill I.S., Cacciamani G.E. Generative artificial intelligence in surgery. Surgery. 2024;175(6):1496–1502. doi: 10.1016/j.surg.2024.02.019

96. Buzzaccarini G., Degliuomini R.S., Borin M. The artificial intelligence application in aesthetic medicine: how ChatGPT can revolutionize the aesthetic world. Aesthetic Plast. Surg. 2023;47(5):2211–2212. doi: 10.1007/s00266-023-03416-w.