Page 51 - Read Online
P. 51
Brenac et al. Art Int Surg 2024;4:296-315 https://dx.doi.org/10.20517/ais.2024.49 Page 314
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. DOI PubMed PMC
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. DOI PubMed PMC
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. DOI PubMed PMC
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. DOI PubMed PMC
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. PubMed
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. DOI PubMed
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. DOI PubMed PMC
34. Mavioso C, Araújo RJ, Oliveira HP, et al. Automatic detection of perforators for microsurgical reconstruction. Breast 2020;50:19-24.
DOI PubMed PMC
35. Kiranantawat K, Sitpahul N, Taeprasartsit P, et al. The first smartphone application for microsurgery monitoring: SilpaRamanitor.
Plast Reconstr Surg 2014;134:130-9. DOI PubMed
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. DOI PubMed PMC
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. DOI PubMed
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. DOI PubMed
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. DOI PubMed
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. DOI PubMed
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. DOI PubMed
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. DOI PubMed PMC
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. DOI PubMed PMC
45. Bäcker HC, Wu CH, Strauch RJ. Systematic review of diagnosis of clinically suspected scaphoid fractures. J Wrist Surg 2020;9:81-9.
DOI PubMed PMC
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. DOI PubMed
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. DOI PubMed
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. DOI PubMed
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. DOI PubMed PMC
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. DOI PubMed PMC
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. DOI PubMed PMC
52. Robb L. Potential for machine learning in burn care. J Burn Care Res 2022;43:632-9. DOI PubMed
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. DOI PubMed
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. DOI PubMed PMC
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. DOI PubMed PMC
56. Huff TJ, Ludwig PE, Zuniga JM. The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional