REFERENCES

1. Maita KC, Avila FR, Torres-Guzman RA, et al. The usefulness of artificial intelligence in breast reconstruction: a systematic review. Breast Cancer 2024;31:562-71.

2. Guo JL, Januszyk M, Longaker MT. Machine learning in tissue engineering. Tissue Eng Part A 2023;29:2-19.

3. Choi E, Leonard KW, Jassal JS, Levin AM, Ramachandra V, Jones LR. Artificial intelligence in facial plastic surgery: a review of current applications, future applications, and ethical considerations. Facial Plast Surg 2023;39:454-9.

4. Sacristán JA, Dilla T. No big data without small data: learning health care systems begin and end with the individual patient. J Eval Clin Pract 2015;21:1014-7.

5. Hume KM, Crotty CA, Simmons CJ, Neumeister MW, Chung KC. Medical specialty society-sponsored data registries: opportunities in plastic surgery. Plast Reconstr Surg 2013;132:159e-67e.

6. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J 2019;6:94-8.

7. Phillips M, Marsden H, Jaffe W, et al. Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions. JAMA Netw Open 2019;2:e1913436.

8. Joshi G, Jain A, Araveeti SR, Adhikari S, Garg H, Bhandari M. FDA-approved artificial intelligence and machine learning (AI/ML)-enabled medical devices: an updated landscape. Electronics 2024;13:498.

9. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-8.

10. Koh DM, Papanikolaou N, Bick U, et al. Artificial intelligence and machine learning in cancer imaging. Commun Med 2022;2:133.

11. Barreiro-Ares A, Morales-Santiago A, Sendra-Portero F, Souto-Bayarri M. Impact of the rise of artificial intelligence in radiology: what do students think? Int J Environ Res Public Health 2023;20:1589.

12. Shin HJ, Han K, Ryu L, Kim EK. The impact of artificial intelligence on the reading times of radiologists for chest radiographs. NPJ Digit Med 2023;6:82.

13. Yacoub B, Varga-Szemes A, Schoepf UJ, et al. Impact of artificial intelligence assistance on chest CT interpretation times: a prospective randomized study. AJR Am J Roentgenol 2022;219:743-51.

14. 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.

15. Duong TV, Vy VPT, Hung TNK. Artificial intelligence in plastic surgery: advancements, applications, and future. Cosmetics 2024;11:109.

16. Ho AL, Klassen AF, Cano S, Scott AM, Pusic AL. Optimizing patient-centered care in breast reconstruction: the importance of preoperative information and patient-physician communication. Plast Reconstr Surg 2013;132:212e-20e.

17. 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.

18. Berry CE, Fazilat AZ, Churukian AA, et al. Quality assessment of online resources for gender-affirming surgery. Plast Reconstr Surg Glob Open 2023;11:e5306.

19. Baldwin AJ. An artificial intelligence language model improves readability of burns first aid information. Burns 2024;50:1122-7.

20. Berry CE, Fazilat AZ, Lavin C, et al. Both patients and plastic surgeons prefer artificial intelligence-generated microsurgical information. J Reconstr Microsurg 2024;40:657-64.

21. Grippaudo FR, Nigrelli S, Patrignani A, Ribuffo D. Quality of the information provided by ChatGPT for patients in breast plastic surgery: are we already in the future? JPRAS Open 2024;40:99-105.

22. Seth I, Cox A, Xie Y, et al. Evaluating chatbot efficacy for answering frequently asked questions in plastic surgery: a ChatGPT case study focused on breast augmentation. Aesthet Surg J 2023;43:1126-35.

23. Xie Y, Seth I, Hunter-Smith DJ, Rozen WM, Ross R, Lee M. Aesthetic surgery advice and counseling from artificial intelligence: a rhinoplasty consultation with ChatGPT. Aesthetic Plast Surg 2023;47:1985-93.

24. Chaker SC, Hung YC, Saad M, Golinko MS, Galdyn IA. Easing the burden on caregivers- applications of artificial intelligence for physicians and caregivers of children with cleft lip and palate. Cleft Palate Craniofac J 2024.

25. Sharma SC, Ramchandani JP, Thakker A, Lahiri A. ChatGPT in plastic and reconstructive surgery. Indian J Plast Surg 2023;56:320-5.

26. Altamimi I, Altamimi A, Alhumimidi AS, Altamimi A, Temsah MH. Artificial intelligence (AI) chatbots in medicine: a supplement, not a substitute. Cureus 2023;15:e40922.

27. Ahmed SK, Hussein S, Aziz TA, Chakraborty S, Islam MR, Dhama K. The power of ChatGPT in revolutionizing rural healthcare delivery. Health Sci Rep 2023;6:e1684.

28. Wang A, Kim E, Oleru O, Seyidova N, Taub PJ. Artificial intelligence in plastic surgery: ChatGPT as a tool to address disparities in health literacy. Plast Reconstr Surg 2024;153:1232e-4e.

29. Daraz L, Morrow AS, Ponce OJ, et al. Can patients trust online health information? A meta-narrative systematic review addressing the quality of health information on the internet. J Gen Intern Med 2019;34:1884-91.

30. Shahsavar Y, Choudhury A. User intentions to use ChatGPT for self-diagnosis and health-related purposes: cross-sectional survey study. JMIR Hum Factors 2023;10:e47564.

31. Fazilat AZ, Berry CE, Churukian A, et al. AI-based cleft lip and palate surgical information is preferred by both plastic surgeons and patients in a blind comparison. Cleft Palate Cran J 2024.

32. Boczar D, Sisti A, Oliver JD, et al. Artificial intelligent virtual assistant for plastic surgery patient’s frequently asked questions: a pilot study. Ann Plast Surg 2020;84:e16-21.

33. Soh CL, Shah V, Arjomandi Rad A, et al. Present and future of machine learning in breast surgery: systematic review. Br J Surg 2022;109:1053-62.

34. Mavioso C, Araújo RJ, Oliveira HP, et al. Automatic detection of perforators for microsurgical reconstruction. Breast 2020;50:19-24.

35. Kiranantawat K, Sitpahul N, Taeprasartsit P, et al. The first smartphone application for microsurgery monitoring: SilpaRamanitor. Plast Reconstr Surg 2014;134:130-9.

36. Myung Y, Jeon S, Heo C, et al. Validating machine learning approaches for prediction of donor related complication in microsurgical breast reconstruction: a retrospective cohort study. Sci Rep 2021;11:5615.

37. Hassan AM, Biaggi-Ondina A, Asaad M, et al. Artificial intelligence modeling to predict periprosthetic infection and explantation following implant-based reconstruction. Plast Reconstr Surg 2023;152:929-38.

38. Bennett SP, Fitoussi AD, Berry MG, Couturaud B, Salmon RJ. Management of exposed, infected implant-based breast reconstruction and strategies for salvage. J Plast Reconstr Aesthet Surg 2011;64:1270-7.

39. Zhang BH, Chen K, Lu SM, et al. Turning back the clock: artificial intelligence recognition of age reduction after face-lift surgery correlates with patient satisfaction. Plast Reconstr Surg 2021;148:45-54.

40. Boonipat T, Asaad M, Lin J, Glass GE, Mardini S, Stotland M. Using artificial intelligence to measure facial expression following facial reanimation surgery. Plast Reconstr Surg 2020;146:1147-50.

41. Geisler EL, Agarwal S, Hallac RR, Daescu O, Kane AA. A role for artificial intelligence in the classification of craniofacial anomalies. J Craniofac Surg 2021;32:967-9.

42. Knoops PGM, Papaioannou A, Borghi A, et al. A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery. Sci Rep 2019;9:13597.

43. Marcus G, Davis E, Aaronson S. A very preliminary analysis of DALL-E 2. arXiv. [Preprint.] May 2, 2022 [accessed on 2024 Sep 30]. Available from: https://doi.org/10.48550/arXiv.2204.13807.

44. Lim B, Seth I, Kah S, et al. Using generative artificial intelligence tools in cosmetic surgery: a study on rhinoplasty, facelifts, and blepharoplasty procedures. J Clin Med 2023;12:6524.

45. Bäcker HC, Wu CH, Strauch RJ. Systematic review of diagnosis of clinically suspected scaphoid fractures. J Wrist Surg 2020;9:81-9.

46. Ozkaya E, Topal FE, Bulut T, Gursoy M, Ozuysal M, Karakaya Z. Evaluation of an artificial intelligence system for diagnosing scaphoid fracture on direct radiography. Eur J Trauma Emerg Surg 2022;48:585-92.

47. Oeding JF, Kunze KN, Messer CJ, et al. Diagnostic performance of artificial intelligence for detection of scaphoid and distal radius fractures: a systematic review. J Hand Surg Am 2024;49:411-22.

48. Hoogendam L, Bakx JAC, Souer JS, Slijper HP, Andrinopoulou ER, Selles RW. Hand Wrist Study Group. Predicting clinically relevant patient-reported symptom improvement after carpal tunnel release: a machine learning approach. Neurosurgery 2022;90:106-13.

49. Loos NL, Hoogendam L, Souer JS, et al; the Hand-Wrist Study Group. Machine learning can be used to predict function but not pain after surgery for thumb carpometacarpal osteoarthritis. Clin Orthop Relat Res 2022;480:1271-84.

50. Kim J, Oh I, Lee YN, et al. Predicting the severity of postoperative scars using artificial intelligence based on images and clinical data. Sci Rep 2023;13:13448.

51. Squiers JJ, Thatcher JE, Bastawros DS, et al. Machine learning analysis of multispectral imaging and clinical risk factors to predict amputation wound healing. J Vasc Surg 2022;75:279-85.

52. Robb L. Potential for machine learning in burn care. J Burn Care Res 2022;43:632-9.

53. Xue Y, Chen C, Tan R, et al. Artificial intelligence-assisted bioinformatics, microneedle, and diabetic wound healing: a “new deal” of an old drug. ACS Appl Mater Interfaces 2022;14:37396-409.

54. Chae MP, Rozen WM, McMenamin PG, Findlay MW, Spychal RT, Hunter-Smith DJ. Emerging applications of bedside 3D printing in plastic surgery. Front Surg 2015;2:25.

55. Knoops PGM, Borghi A, Ruggiero F, et al. A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling. PLoS One 2018;13:e0197209.

56. Huff TJ, Ludwig PE, Zuniga JM. The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning. Expert Rev Med Devices 2018;15:349-56.

57. Booth J, Roussos A, Zafeiriou S, Ponniah A, Dunaway D. A 3D morphable model learnt from 10,000 faces. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016 Jun 27-30; Las Vegas, USA. IEEE; 2016. pp. 5543-52.

58. Dai H, Pears N, Smith W, Duncan C. A 3D morphable model of craniofacial shape and texture variation. In: 2017 IEEE International Conference on Computer Vision (ICCV); 2017 Oct 22-29; Venice, Italy. IEEE; 2017. pp. 3104-12.

59. Goh GD, Sing SL, Yeong WY. A review on machine learning in 3D printing: applications, potential, and challenges. Artif Intell Rev 2021;54:63-94.

60. Menon A, Póczos B, Feinberg AW, Washburn NR. Optimization of silicone 3D printing with hierarchical machine learning. 3D Print Addit Manuf 2019;6:181-9.

61. Conev A, Litsa EE, Perez MR, Diba M, Mikos AG, Kavraki LE. Machine learning-guided three-dimensional printing of tissue engineering scaffolds. Tissue Eng Part A 2020;26:1359-68.

62. Chae MP, Hunter-Smith DJ, Spychal RT, Rozen WM. 3D volumetric analysis for planning breast reconstructive surgery. Breast Cancer Res Treat 2014;146:457-60.

63. Lei IM, Jiang C, Lei CL, et al. 3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients. Nat Commun 2021;12:6260.

64. Asghari A, O’Connor MJ, Attalla P, et al. Game changers: plastic and reconstructive surgery innovations of the last 100 years. Plast Reconstr Surg Glob Open 2023;11:e5209.

65. Lao WWK, Hsieh TY, Ramirez AE. Differences and similarities between eastern and western rhinoplasty: features and proposed algorithms. Ann Plast Surg 2021;86:S259-64.

66. Mir MA, Maurya R. Precision and progress: machine learning advancements in plastic surgery. Cureus 2023;15:e41952.

67. Pool C, Moroco A, Lighthall JG. Utilizing virtual surgical planning and patient-specific cutting guides in microtia repair with autologous costal cartilage graft. Plast Reconstr Surg 2024;154:569e-72e.

68. O’Sullivan S, Leonard S, Holzinger A, et al. Operational framework and training standard requirements for AI‐empowered robotic surgery. Int J Med Robot 2020;16:1-13.

69. Koçak B, Cuocolo R, dos Santos DP, Stanzione A, Ugga L. Must-have qualities of clinical research on artificial intelligence and machine learning. Balkan Med J 2023;40:3-12.

Artificial Intelligence Surgery
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