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
   46   47   48   49   50   51   52   53   54   55   56