Facial Plast Surg 2023; 39(05): 454-459
DOI: 10.1055/s-0043-1770160
Original Article

Artificial Intelligence in Facial Plastic Surgery: A Review of Current Applications, Future Applications, and Ethical Considerations

Elizabeth Choi
1   Wayne State University School of Medicine, Detroit, Michigan
,
Kyle W. Leonard
2   Department of Otolaryngology, Henry Ford Hospital, Detroit, Michigan
,
Japnam S. Jassal
2   Department of Otolaryngology, Henry Ford Hospital, Detroit, Michigan
,
Albert M. Levin
3   Department of Public Health Science, Henry Ford Health, Detroit, Michigan
4   Center for Bioinformatics, Henry Ford Health, Detroit, Michigan
,
Vikas Ramachandra
3   Department of Public Health Science, Henry Ford Health, Detroit, Michigan
4   Center for Bioinformatics, Henry Ford Health, Detroit, Michigan
,
Lamont R. Jones
2   Department of Otolaryngology, Henry Ford Hospital, Detroit, Michigan
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Abstract

From virtual chat assistants to self-driving cars, artificial intelligence (AI) is often heralded as the technology that has and will continue to transform this generation. Among widely adopted applications in other industries, its potential use in medicine is being increasingly explored, where the vast amounts of data present in electronic health records and need for continuous improvements in patient care and workflow efficiency present many opportunities for AI implementation. Indeed, AI has already demonstrated capabilities for assisting in tasks such as documentation, image classification, and surgical outcome prediction. More specifically, this technology can be harnessed in facial plastic surgery, where the unique characteristics of the field lends itself well to specific applications. AI is not without its limitations, however, and the further adoption of AI in medicine and facial plastic surgery must necessarily be accompanied by discussion on the ethical implications and proper usage of AI in healthcare. In this article, we review current and potential uses of AI in facial plastic surgery, as well as its ethical ramifications.

Keywords

artificial intelligence - ethics - machine learning - patient care


Publikationsverlauf

Artikel online veröffentlicht:
23. Juni 2023

© 2023. Thieme. All rights reserved.

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  • References

  • 1 Sardar P, Abbott JD, Kundu A, Aronow HD, Granada JF, Giri J. Impact of artificial intelligence on interventional cardiology: from decision-making aid to advanced interventional procedure assistance. JACC Cardiovasc Interv 2019; 12 (14) 1293-1303
    CrossrefPubMedGoogle Scholar
  • 2 Garcia-Vidal C, Sanjuan G, Puerta-Alcalde P, Moreno-García E, Soriano A. Artificial intelligence to support clinical decision-making processes. EBioMedicine 2019; 46: 27-29
    CrossrefPubMedGoogle Scholar
  • 3 Milne-Ives M, de Cock C, Lim E. et al. The effectiveness of artificial intelligence conversational agents in health care: systematic review. J Med Internet Res 2020; 22 (10) e20346
    CrossrefPubMedGoogle Scholar
  • 4 Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA. Surgical risk is not linear: derivation and validation of a novel, user-friendly, and machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator. Ann Surg 2018; 268 (04) 574-583
    CrossrefPubMedGoogle Scholar
  • 5 Adhikari L, Ozrazgat-Baslanti T, Ruppert M. et al. Improved predictive models for acute kidney injury with IDEA: intraoperative data embedded analytics. PLoS One 2019; 14 (04) e0214904
    CrossrefPubMedGoogle Scholar
  • 6 Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI Chatbot for medicine. N Engl J Med 2023; 388 (13) 1233-1239
    CrossrefPubMedGoogle Scholar
  • 7 Syed AB, Zoga AC. Artificial intelligence in radiology: current technology and future directions. Semin Musculoskelet Radiol 2018; 22 (05) 540-545
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 8 Esteva A, Kuprel B, Novoa RA. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542 (7639): 115-118
    CrossrefPubMedGoogle Scholar
  • 9 Loftus TJ, Tighe PJ, Filiberto AC. et al. Artificial intelligence and surgical decision-making. JAMA Surg 2020; 155 (02) 148-158
    CrossrefPubMedGoogle Scholar
  • 10 Liang X, Yang X, Yin S. et al. Artificial intelligence in plastic surgery: applications and challenges. Aesthetic Plast Surg 2021; 45 (02) 784-790
    CrossrefPubMedGoogle Scholar
  • 11 Murphy DC, Saleh DB. Artificial intelligence in plastic surgery: what is it? Where are we now? What is on the horizon?. Ann R Coll Surg Engl 2020; 102 (08) 577-580
    CrossrefPubMedGoogle Scholar
  • 12 Chandawarkar A, Chartier C, Kanevsky J, Cress PE. A practical approach to artificial intelligence in plastic surgery. Aesthet Surg J Open Forum 2020; 2 (01) ojaa001
    CrossrefPubMedGoogle Scholar
  • 13 Kitchin R. Thinking critically about and researching algorithms. Inf Commun Soc 2017; 20 (01) 14-19
    CrossrefPubMedGoogle Scholar
  • 14 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 (10) e3200
    CrossrefPubMedGoogle Scholar
  • 15 Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018; 18 (08) 500-510
    CrossrefPubMedGoogle Scholar
  • 16 Sinsky C, Tutty M, Colligan L. Allocation of physician time in ambulatory practice. Ann Intern Med 2017; 166 (09) 683-684
    CrossrefPubMedGoogle Scholar
  • 17 Overhage JM, McCallie D. Physician time spent using the electronic health record during outpatient encounters. Ann Intern Med 2020; 173 (07) 594-595
    CrossrefPubMedGoogle Scholar
  • 18 Klann JG, Szolovits P. An intelligent listening framework for capturing encounter notes from a doctor-patient dialog. BMC Med Inform Decis Mak 2009; 9 (Suppl. 01) S3
    CrossrefPubMedGoogle Scholar
  • 19 Sezgin E, Hussain SA, Rust S, Huang Y. Extracting medical information from free-text and unstructured patient-generated health data using natural language processing methods: feasibility study with real-world data. JMIR Form Res 2023; 7: e43014
    CrossrefPubMedGoogle Scholar
  • 20 AI Medical Documentation for Physicians & Hospitals. . Augmedix. Accessed June 6, 2023 at https://augmedix.com/
    PubMed
  • 21 AI-Powered Medical Documentation Tech - DeepScribe. . Accessed June 6, 2023. https://www.deepscribe.ai/technology
    PubMed
  • 22 Patel SB, Lam K. ChatGPT: the future of discharge summaries?. Lancet Digit Health 2023; 5 (03) e107-e108
    CrossrefPubMedGoogle Scholar
  • 23 Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA 2017; 318 (06) 517-518
    CrossrefPubMedGoogle Scholar
  • 24 Baker A, Perov Y, Middleton K. et al. A comparison of artificial intelligence and human doctors for the purpose of triage and diagnosis. Front Artif Intell 2020; 3: 543405
    CrossrefPubMedGoogle Scholar
  • 25 Borsting E, DeSimone R, Ascha M, Ascha M. Applied deep learning in plastic surgery: classifying rhinoplasty with a mobile app. J Craniofac Surg 2020; 31 (01) 102-106
    CrossrefPubMedGoogle Scholar
  • 26 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 (10) e1913436
    CrossrefPubMedGoogle Scholar
  • 27 Formeister EJ, Baum R, Knott PD. et al. Machine learning for predicting complications in head and neck microvascular free tissue transfer. Laryngoscope 2020; 130 (12) E843-E849
    CrossrefPubMedGoogle Scholar
  • 28 Wormald JCR, Rodrigues JN. Outcome measurement in plastic surgery. J Plast Reconstr Aesthet Surg 2018; 71 (03) 283-289
    CrossrefPubMedGoogle Scholar
  • 29 Gibstein AR, Chen K, Nakfoor B. et al. Facelift surgery turns back the clock: artificial intelligence and patient satisfaction quantitate value of procedure type and specific techniques. Aesthet Surg J 2021; 41 (09) 987-999
    CrossrefPubMedGoogle Scholar
  • 30 Rogers MP, DeSantis AJ, Janjua H, Barry TM, Kuo PC. The future surgical training paradigm: virtual reality and machine learning in surgical education. Surgery 2021; 169 (05) 1250-1252
    CrossrefPubMedGoogle Scholar
  • 31 Kiyasseh D, Laca J, Haque TF. et al. A multi-institutional study using artificial intelligence to provide reliable and fair feedback to surgeons. Commun Med (Lond) 2023; 3 (01) 42
    CrossrefPubMedGoogle Scholar
  • 32 Soangra R, Sivakumar R, Anirudh ER, Reddy Y SV, John EB. Evaluation of surgical skill using machine learning with optimal wearable sensor locations. PLoS One 2022; 17 (06) e0267936
    CrossrefPubMedGoogle Scholar
  • 33 Gupta R, Park JB, Bisht C. et al. Expanding cosmetic plastic surgery research using ChatGPT. Aesthet Surg J 2023; ,Mar 21: sjad069
    PubMedGoogle Scholar
  • 34 Gupta R, Herzog I, Weisberger J, Chao J, Chaiyasate K, Lee ES. Utilization of ChatGPT for plastic surgery research: friend or foe?. J Plast Reconstr Aesthet Surg 2023; 80: 145-147
    CrossrefPubMedGoogle Scholar
  • 35 Hopkins BS, Mazmudar A, Driscoll C. et al. Using artificial intelligence (AI) to predict postoperative surgical site infection: a retrospective cohort of 4046 posterior spinal fusions. Clin Neurol Neurosurg 2020; 192: 105718
    CrossrefPubMedGoogle Scholar
  • 36 Korot E, Wagner SK, Faes L. et al. Will AI replace ophthalmologists?. Transl Vis Sci Technol 2020; 9 (02) 2
    CrossrefPubMedGoogle Scholar
  • 37 Pesapane F, Tantrige P, Patella F. et al. Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists. Med Oncol 2020; 37 (05) 40
    CrossrefPubMedGoogle Scholar
  • 38 Pinto Dos Santos D, Giese D, Brodehl S. et al. Medical students' attitude towards artificial intelligence: a multicentre survey. Eur Radiol 2019; 29 (04) 1640-1646
    CrossrefPubMedGoogle Scholar
  • 39 Loftus TJ, Ruppert MM, Shickel B. et al. Overtriage, undertriage, and value of care after major surgery: an automated, explainable deep learning-enabled classification system. J Am Coll Surg 2023; 236 (02) 279-291
    CrossrefPubMedGoogle Scholar
  • 40 Morris MX, Song EY, Rajesh A, Asaad M, Phillips BT. Ethical, legal, and financial considerations of artificial intelligence in surgery. Am Surg 2023; 89 (01) 55-60
    CrossrefPubMedGoogle Scholar
  • 41 Parikh RB, Teeple S, Navathe AS. Addressing bias in artificial intelligence in health care. JAMA 2019; 322 (24) 2377-2378
    CrossrefPubMedGoogle Scholar
  • 42 Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med 2018; 178 (11) 1544-1547
    CrossrefPubMedGoogle Scholar
  • 43 Gunes H, Piccardi M. Assessing facial beauty through proportion analysis by image processing and supervised learning. Int J Hum Comput Stud 2006; 64 (12) 1184-1199
    CrossrefPubMedGoogle Scholar
  • 44 Koimizu J, Numajiri T, Kato K. Machine learning and ethics in plastic surgery. Plast Reconstr Surg Glob Open 2019; 7 (03) e2162
    CrossrefPubMedGoogle Scholar
  • 45 Prigoff JG, Sherwin M, Divino CM. Ethical recommendations for video recording in the operating room. Ann Surg 2016; 264 (01) 34-35
    CrossrefPubMedGoogle Scholar