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Page 297 Brenac et al. Art Int Surg 2024;4:296-315 https://dx.doi.org/10.20517/ais.2024.49

INTRODUCTION
Artificial intelligence (AI) refers to a class of computer science and engineering technologies that leverage
sophisticated algorithms, including machine learning (ML) methods, to perform tasks that typically require
human intelligence. Although there are several subtypes of AI, ML has been widely used in the clinical
setting and focuses on finding patterns in large datasets and/or performing predictive modeling based on
[1,2]
prior training data . ML encompasses a plethora of model types, including artificial neural networks
[3]
(ANN), deep neural networks (DNN), natural language processing (NLP), and computer vision (CV) .
Within healthcare, AI tools have demonstrated significant capability across various contexts, including
remote patient monitoring, medical diagnostics, risk management, conversation agents, and provision of
[1,2]
virtual assistants . Therefore, AI is postulated to have the potential to guide the diagnostic process,
decrease the likelihood of medical errors, and improve the precision of medical decisions. Additionally,
there is growing interest in the ability of ChatGPT, a popular NLP algorithm, to generate patient-facing
information .
[3]
ML applications in healthcare often resemble traditional statistical analysis, although ML algorithms are
more adept at handling large, heterogeneous datasets and detecting less strictly formalized relationships
between these data . Since the amount of data in electronic health records (EHRs) has recently doubled
[2]
every two years, AI tools that collect and analyze a number of data unachievable by human power alone will
likely bring numerous changes to the medical field . For example, one healthcare application of this AI
[4,5]
tool is prediction algorithms, applicable in various specialties, where ML is able to provide the probability of
outcomes . Therefore, the most successful clinical applications of AI are seen in medical specialties that
[6]
collect large amounts of standardized data, including image-recognition tasks in dermatology, radiology,
pathology, and cardiology, as reflected by the number of Food and Drug Administration (FDA)-approved
medical devices across these specialties [Figure 1] . For instance, in dermatology, ML has demonstrated
[7,8]
performance comparable to board-certified dermatologists in the detection of skin lesions from clinical or
dermoscopic images , as well as recognition of potentially cancerous lesions in radiologic images . In
[10]
[9]
radiology, AI has gained significant prominence, transforming how the specialty is practiced and reducing
radiologists’ workloads, particularly by decreasing the time required to interpret X-rays and computed
tomography (CT) scans [11-13] .

While many surgical disciplines involve less standardized data compared to imaging-focused medical
practices, the field has evolved significantly over the past few years to integrate AI into practice .
[14]
Specifically, AI can revolutionize plastic surgery by enhancing patient information, patient-surgeon
communication, surgical planning, and 3D tissue modeling and printing for surgical applications [14,15] .
Therefore, analyzing current applications of AI in surgery is critical to developing novel surgical resources
that have the potential to provide patients with the highest-quality healthcare. In this review, we explore
how AI has impacted multiple facets of plastic and reconstructive surgery (PRS) and demonstrate ways in
which patient-specific care in surgery has been influenced by the adoption of AI tools.

METHODOLOGY
The literature analysis was conducted as a narrative review, utilizing the following databases: Cochrane
Library, Embase, Web of Science, and Medline. A search strategy incorporating both MeSH terms and free-
text keywords was employed, focusing on the terms “Surgery, Plastic” and “Artificial Intelligence”. The
search was limited to articles published within the last 10 years, from 2013 to the present. The objective was
to identify all relevant studies that reported on the tailored application of AI in plastic surgery. Articles
identified through the search were categorized into three key areas: Patient Preparation and Education, Pre-
and Postoperative Assessments, and 3D Tissue Modeling and Printing. Studies that were outside the