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reviews, abstracts without associated full texts, and non-English-language publications. No search
restrictions were placed regarding publication date or country of origin. Data extraction was then
performed by two independent reviewers (EH, SN). The following characteristics were extracted from each
study: study design, surgical specialty, type of AI model, model performance, and application to surgical
education. Data were then synthesized into a narrative description given the heterogeneous and qualitative
nature of results and categorized by which areas of surgical training the AI model may be applied.

RESULTS
The initial search yielded 1,037 papers, and the full-text review yielded 20 unique papers published between
2019 and 2025 that met inclusion criteria [Figure 1] [9-28] . The majority of studies were proof-of-concept
papers and piloted a novel use for AI. All studies included original data; however, three were published as
correspondences or viewpoints due to their limited scope. Four studies analyzed ChatGPT, four involved
the creation of a novel AI algorithm, three assessed natural language processing (NLP) models, and the
remainder assessed applications of various AI platforms. Most papers concern the field of plastic surgery
(n = 8). However, general surgery (n = 4), neurosurgery (n = 3), endodontics (n = 1), obstetrics/gynecology
(n = 1), orthopedic surgery (n = 1), urology (n = 1), and craniofacial surgery (n = 1) were also included
[Table 1]. The literature was classified into three distinct categories in which AI was used in resident
education: supplemental learning, surgical skills training, and performance feedback.

To further characterize the scope of AI integration, each study was also analyzed according to the AI
methodology utilized. Classifications included predictive AI and generative AI. Predictive AI models
involve machine learning and deep learning algorithms that may classify, assess, or predict outcomes based
on data. Specific models identified in this review included traditional machine learning algorithms (Saadya,
Siyar, Fukata, Stahl), convolutional neural networks (Sayadi, Lei, Fang), deep neural networks (Yilmaz,
Fazlollahi), and planning-based AI used in intelligent tutoring systems (Vannaprathip). NLP models were
also used in predictive capacities (Ötleş, Solano, and Li). Generative AI models, on the other hand, leverage
large language models and multimodal architectures to produce new text, dialogue, or images. Numerous
studies utilized ChatGPT (Saadya, Hubany, Gupta, Humar, DiDonna, Shah, Zhang, Hui). Other large
language models such as Google Bard, Google PaLM, Microsoft Bing, Claude, My AI by Snapchat, and
Wondercraft were also studied (DiDonna, Shah, Saadya). A distinct generative AI model, DALL·E 2, was
studied by Koljonen et al. for text-to-image generation .
[13]

Augmentation of supplemental learning
Several contemporary studies have outlined how AI can augment supplemental learning methods [9-15] . AI
image generation has the potential to enhance traditional textbook learning. Koljonen et al. used an AI
algorithm to convert generic English text into clinical photographs, and photos of soft tissue and skin
[13]
tumors were both realistic and accurate .

In addition, AI has the potential to create learning materials on nontraditional platforms such as podcasts.
Podcasts addressing plastic surgery content have become increasingly common with the general rise in
popularity of podcasting . Saadya et al. showed that a podcast for plastic surgery education can be made
[29]
using ChatGPT and Wondercraft.ai . ChatGPT synthesized information to create a question-and-answer-
[14]
styled script. Wondercraft.ai then converted this written script into an audio podcast that provided a
“realistic auditory experience” while conveying complex surgical concepts .
[14]

AI-based tools have also shown promise in fostering independent academic learning. In craniofacial
surgery, ChatGPT assisted residents in developing novel systematic review ideas, highlighting its potential