Page 74 - Read Online

Hogue et al. Art Int Surg. 2025;5:350-60 https://dx.doi.org/10.20517/ais.2025.19 Page 356

structures to comment on resident performance at different entrustment levels.

Second, Ötleş et al. were among the first to show the potential of NLP models to classify the quality of
[26]
surgical trainee feedback . Surgical faculty evaluations sourced from three general surgery residency
programs consisted of narrative feedback, and machine learning systems were compared in their ability to
classify feedback as either effective, mediocre, ineffective, or other. The support vector machine (SVM)
model was most effective and achieved a mean accuracy of 0.64 when sorting feedback into the original
categories and a mean accuracy of 0.83 when sorting data into either high-quality or low-quality feedback. A
subsequent study tested the performance of the SVM model, which was identified to be most accurate by
Solano et al., on a larger dataset . The SVM model performed similarly to the earlier study. Feedback was
[27]
sorted into the original categories outlined by Ötleş et al. with an accuracy of 0.65 . When identifying only
[26]
low-quality feedback, the model achieved an accuracy of 0.83, sensitivity of 0.37, and specificity of 0.97,
reaffirming its ability to effectively measure feedback quality.

DISCUSSION
The applications of AI in plastic surgery training remain in their infancy. Only eight studies were identified
that specifically assessed uses for AI in plastic surgery. However, nascent studies of AI shed light on its
potential for successful integration into surgical training of all kinds. Educational applications of AI have
been more widely adapted and implemented in other surgical fields but demonstrate possible areas for
growth in plastic surgery. Traditionally, plastic surgery trainees learn in the operating room and the lecture
hall with independent study using textbooks, digital resources, and practice question databases. AI has
proven successful in enhancing digital educational materials such as podcasts, which residents find to be a
useful method of asynchronous learning but currently lack high-quality plastic surgery educational
content [30-33] . High-speed, tailored generation of targeted information to benefit trainees’ individual needs
could provide an excellent means to study efficiently. Text-to-image software could circumvent the risk of
breaching patient privacy tied to utilizing real patient photographs and could illustrate a broader range of
pathologies from a more diverse patient population . This could have novel applications in preparing
[13]
board-style vignettes with accurate images or generating AI-drawn anatomical plates for particular
pathologies or surgical approaches of interest to learners. AI’s success in plastic surgery trainee
examinations suggests its usefulness as a primary resource for plastic surgery information [9-15,29-33] . For
example, based on its examination performance, ChatGPT could provide general knowledge, clarify
complex topics, simulate case-based learning, summarize the literature, and formulate novel practice
questions [9-12,15] .

A key finding of this review is the methodological diversity among AI applications in surgical education,
including a variety of predictive and generative AI. Predictive models including traditional learning
algorithms, convolutional deep neural networks, and planning-based systems were primarily used to assess
surgical performance, simulate procedural tasks, or provide individualized feedback [14,19-25,28,34] . These models
require robust datasets and physician oversight, but have shown promise in skill differentiation and
assessment validation [18,20,23-27] . Generative AI models, including LLMs and multimodal tools such as DALL·E
2, were leveraged to enhance educational content [9-17] . These tools facilitated the creation of podcasts, visual
aids, and synthesized summaries, which highlights their potential for scalable, learner-centered educational
innovations [10,12,14-17] .

Given the rapid adoption of LLMs such as ChatGPT, Claude, and Bard, their role in surgical education
deserves particular attention. For trainees, LLMs enable real-time access to personalized educational
content, including case simulations, oral board-style questioning, concept clarification, and targeted