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Page 351 Hogue et al. Art Int Surg. 2025;5:350-60 https://dx.doi.org/10.20517/ais.2025.19

Keywords: Artificial intelligence, machine learning, natural language processing, surgical training, plastic surgery
education

INTRODUCTION
Artificial intelligence (AI) is the ability of a machine to perform human-like decision making . AI
[1]
algorithms utilize computer code to mimic human neural networks, abstracting user inputs and executing
complex commands. Generative AI is capable of accessing large swaths of data to generate original material
based on user requests. Its capabilities include performing mathematical calculations, generating original
images, and creating novel text - all at speeds faster than humans . This novel technology has, therefore,
[2]
[3]
encouraged the integration of digital resources into all facets of medicine, including education .
As surgical advancements are made, surgeons are responsible for an ever-growing fund of knowledge and
are expected to master an increasingly detailed skillset. Traditionally, resident education has been based on
the Halsted apprenticeship model, with education occurring in the operating room, formal didactic
sessions, and through individual pursuit of supplemental educational materials. However, increasing
curriculum content coupled with limited resident work hours places constraints on resident learning, and
leaders are calling for educational reform .
[4]
Innovations in AI may offer one solution to bridging current gaps in plastic surgery education. AI offers
innovative and time-saving methods by which surgical trainees may both obtain information and practice
surgical skills outside the operating room. AI may further enhance resident education by objectively
assessing operative skills during simulation training or by evaluating the quality of resident feedback. AI
language processing may assist residents with quality assessment of published works in preparation for
exams and didactic education.

The integration of AI into resident education requires both the acceptance and investment of both surgeons
and residents. Despite minimal experience applying AI in medicine, surgeons and residents have overall
positive opinions regarding the usefulness of this technology in surgical education . A worldwide survey of
[5,6]
[5]
plastic surgeons revealed their desire to integrate AI into resident education . Surgical residents have also
expressed interest in the application of AI within surgery and believe that AI can advance medical
[6,7]
education, specifically surgical skill acquisition . As a response to the growing demand for the integration
of AI in surgical training, in this review, we look to expound on the new and innovative ways in which AI is
being incorporated to enhance plastic surgery resident education.

METHODS
[8]
A scoping review was conducted according to the PRISMA-ScR guidelines . The current literature
discussing the application of AI in plastic surgery training was reviewed. A search of the PubMed,
Cochrane, Scopus, and Google Scholar databases was performed by two independent authors (EH, SN).
Given the rapidly evolving field of AI, Google Scholar was searched to include relevant innovations reported
outside of traditional peer-reviewed literature. The initial search was completed on February 15th, 2025,
with a follow-up search on June 6th, 2025. Search criteria were developed with the assistance of a medical
librarian and key meshed search terms included “Artificial intelligence”, “machine learning”, “natural
language processing”, “plastic surgery training”, “plastic surgery education”, “plastic surgery resident”,
“surgical training”, and “surgery resident education”. Title, abstract, and full-text review was performed
independently by two reviewers (EH, SN), with the senior author settling any disagreements (FH). The
reference lists of included articles were also searched for relevant studies. Articles met inclusion criteria
when they discussed a real-world application of a specific AI model in surgery resident education and
included metrics on model performance. Exclusions were made for duplicate publications, literature