Artificial Intelligence in Plastic Surgery: Current Applications, Future Directions, and Ethical Implications

Jarvis, Tyler L.; Thornburg, Danielle A.; Rebecca, Alanna M.; Teven, Chad M.

doi:10.1097/gox.0000000000003200

Cited by 55 publications

(54 citation statements)

References 35 publications

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“…Current smartphone technology should not supplant the shared decision-making process between patient and provider, but rather augment it. 32 – 34 …”

Section: Discussionmentioning

confidence: 99%

“…Current smartphone technology should not supplant the shared decision-making process between patient and provider, but rather augment it. [32][33][34] As mobile applications for patient care evolve, so should patient data safety measures. Of the eight applications with the potential to handle patient data, seven demonstrated some degree of data safety measures (Table 2).…”

Section: Data and Ethical Considerationsmentioning

confidence: 99%

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A Scoping Review of Mobile Apps in Plastic Surgery: Patient Care, Trainee Education, and Professional Development

Jarvis

Morris

et al. 2023

Plastic and Reconstructive Surgery - Global Open

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Background: Over the past 10 years, smartphones have become ubiquitous, and mobile apps serve a seemingly endless number of functions in our everyday lives. These functions have entered the realm of plastic surgery, impacting patient care, education, and delivery of services. This article reviews the current uses of plastic surgery mobile apps, app awareness within the plastic surgery community, and the ethical issues surrounding their use in patient care. Methods: A scoping review of electronically available literature within PubMed, Embase, and Scopus databases was conducted in two waves in November and May 2022. Publications discussing mobile application use in plastic surgery were screened for inclusion. Results: Of the 80 nonduplicate publications retrieved, 20 satisfied the inclusion criteria. Articles acquired from the references of these publications were reviewed and summarized when relevant. The average American Society of Plastic Surgeons evidence rating of the publications was 4.2. Applications could be categorized broadly into three categories: patient care and surgical applications, professional development and education, and marketing and practice development. Conclusions: Mobile apps related to plastic surgery have become an abundant resource for patients, attending surgeons, and trainees. Many help bridge gaps in patient care and surgeon-patient communication, and facilitate marketing and practice development. Others make educational content more accessible to trainees and performance assessment more efficient and equitable. The extent of their impact on patient decision-making and expectations has not been completely elucidated.

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“…Current smartphone technology should not supplant the shared decision-making process between patient and provider, but rather augment it. 32 – 34 …”

Section: Discussionmentioning

confidence: 99%

Section: Data and Ethical Considerationsmentioning

confidence: 99%

A Scoping Review of Mobile Apps in Plastic Surgery: Patient Care, Trainee Education, and Professional Development

Jarvis

Morris

et al. 2023

Plastic and Reconstructive Surgery - Global Open

Self Cite

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“…This is the first systematic review focusing on the application of ML in plastic surgery, adding to previous reviews on AI in the specialty. 72 After careful selection of studies that demonstrated the clinical application of these algorithms, we identified 51 articles describing the application of 103 ML algorithms. In our review, the mean accuracy for diagnosis prediction, outcome prediction, and preoperative planning was 88.80%, 86.11%, and 80.28%, respectively.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery

Mantelakis

Sorooshian

Khajuria

2021

Plastic and Reconstructive Surgery - Global Open

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Introduction: Machine learning (ML) is a set of models and methods that can detect patterns in vast amounts of data and use this information to perform various kinds of decision-making under uncertain conditions. This review explores the current role of this technology in plastic surgery by outlining the applications in clinical practice, diagnostic and prognostic accuracies, and proposed future direction for clinical applications and research. Methods: EMBASE, MEDLINE, CENTRAL and ClinicalTrials.gov were searched from 1990 to 2020. Any clinical studies (including case reports) which present the diagnostic and prognostic accuracies of machine learning models in the clinical setting of plastic surgery were included. Data collected were clinical indication, model utilised, reported accuracies, and comparison with clinical evaluation. Results: The database identified 1181 articles, of which 51 articles were included in this review. The clinical utility of these algorithms was to assist clinicians in diagnosis prediction (n=22), outcome prediction (n=21) and pre-operative planning (n=8). The mean accuracy is 88.80%, 86.11% and 80.28% respectively. The most commonly used models were neural networks (n=31), support vector machines (n=13), decision trees/random forests (n=10) and logistic regression (n=9). Conclusions: ML has demonstrated high accuracies in diagnosis and prognostication of burn patients, congenital or acquired facial deformities, and in cosmetic surgery. There are no studies comparing ML to clinician's performance. Future research can be enhanced using larger datasets or utilising data augmentation, employing novel deep learning models, and applying these to other subspecialties of plastic surgery.

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“…AIenabled decision-making tools combined with predictive analytics and integrating human surgical intuition hold great promise for improving surgical outcomes. The surgeon could make real-time decisions perioperatively based on 3D planning, anatomical localization, and surgical navigation [15,18,21,26]. Complex surgical procedures cannot be performed with current AI tools.…”

Section: Future Applicationsmentioning

confidence: 99%

Artificial intelligence applications and ethical challenges in oral and maxillo-facial cosmetic surgery: a narrative review

Rokhshad

Keyhan

Yousefi³

2023

Maxillofac Plast Reconstr Surg

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Artificial intelligence (AI) refers to using technologies to simulate human cognition to solve a specific problem. The rapid development of AI in the health sector has been attributed to the improvement of computing speed, exponential increase in data production, and routine data collection. In this paper, we review the current applications of AI for oral and maxillofacial (OMF) cosmetic surgery to provide surgeons with the fundamental technical elements needed to understand its potential. AI plays an increasingly important role in OMF cosmetic surgery in various settings, and its usage may raise ethical issues. In addition to machine learning algorithms (a subtype of AI), convolutional neural networks (a subtype of deep learning) are widely used in OMF cosmetic surgeries. Depending on their complexity, these networks can extract and process the elementary characteristics of an image. They are, therefore, commonly used in the diagnostic process for medical images and facial photos. AI algorithms have been used to assist surgeons with diagnosis, therapeutic decisions, preoperative planning, and outcome prediction and evaluation. AI algorithms complement human skills while minimizing shortcomings through their capabilities to learn, classify, predict, and detect. This algorithm should, however, be rigorously evaluated clinically, and a systematic ethical reflection should be conducted regarding data protection, diversity, and transparency. It is possible to revolutionize the practice of functional and aesthetic surgeries with 3D simulation models and AI models. Planning, decision-making, and evaluation during and after surgery can be improved with simulation systems. A surgical AI model can also perform time-consuming or challenging tasks for surgeons.

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Artificial Intelligence in Plastic Surgery: Current Applications, Future Directions, and Ethical Implications

Cited by 55 publications

References 35 publications

A Scoping Review of Mobile Apps in Plastic Surgery: Patient Care, Trainee Education, and Professional Development

A Scoping Review of Mobile Apps in Plastic Surgery: Patient Care, Trainee Education, and Professional Development

Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery

Artificial intelligence applications and ethical challenges in oral and maxillo-facial cosmetic surgery: a narrative review

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