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Hogue et al. Art Int Surg. 2025;5:350-60 https://dx.doi.org/10.20517/ais.2025.19 Page 358

surgery, residents may still prefer expert surgeon-produced instructions compared with those generated by
[35]
AI . Furthermore, the inner workings of AI algorithms are not transparent, and physicians may not
understand the extent to which AI capabilities are limited by human error and can perpetuate biases built
into their algorithms. It is imperative to understand these limitations prior to integrating AI into surgical
training.

Current limitations of AI in medicine may have unintended ethical consequences, highlighting the need for
further research into its possible negative impacts . In addition to technical skills, surgeons must be
[36]
compassionate communicators capable of ethical decision making, which cannot be taught by AI alone.
Overreliance on AI education tools to teach non-technical skills would risk the strict ethical standards to
which surgeons are held. Furthermore, AI program training is dependent on datasets, some of which are
extrapolated from patient information and health records, raising concerns about potential ethical
implications and breaches of patient confidentiality . The use of patient photographs or clinical data
[37]
without explicit, procedure-specific consent may result in privacy violations. Current consent forms rarely,
if ever, address the use of personal data for machine learning training, leaving patients potentially unaware
of the downstream applications of their personal data. Adaptations must be made to the release of patient
information, data use agreements, and institutional review board requirements to account for these changes.
Patients should be made aware that their data are being used to train, test, and validate AI models.

Furthermore, the economic practicalities of implementing novel AI-based resident education tools must be
considered. An economic investment is required to initiate the use of AI models. However, AI-assisted
education can provide cost benefits. For example, modalities that provide automated feedback can reduce
training costs overall. In a randomized trial, Lohre et al. showed that resident training with a VR simulator
was cost-effective due to reductions in training time . In addition, AI reduces the training burden on
[38]
surgeon educators, thereby increasing efficiency.

In conclusion, AI algorithms such as ChatGPT could serve as a versatile educational tool for plastic surgery
residents, but the use of AI for plastic surgery resident training remains in its infancy. There exists an
obvious dearth of studies regarding the applications of AI to plastic surgery-specific education. Educational
uses of AI have been more studied in other surgical subspecialties. These applications can be translated into
plastic surgery training. A small number of studies show potential applications of AI across varied areas of
surgical education, including independent resident learning, surgical skill practice, and resident feedback.
Further evidence is needed regarding the implementation and long-term success of specific algorithms.
Further study should be pursued within the field of plastic surgery to evaluate the application of text-to-
image and NLP software for generating practice questions, assess the ability of NLP software to enhance
narrative resident feedback, and assess the application of existing AI models that provide real-time feedback
for surgical skills specific to plastic surgery.

DECLARATIONS
Authors’ contributions
Made substantial contributions to the conception and design of the work, acquisition and interpretation of
the data, and drafting and revisions of the manuscript: Hogue E
Made substantial contributions to the conception of the work, acquisition and interpretation of the data,
and drafting and revisions of the manuscript: Nottingham S
Made substantial revisions to the manuscript and contributed to the interpretation of the data: James A
Made substantial contributions to the conception and design of the work, interpretation of the data, and
revisions of the manuscript: Herrera FA