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Page 430 Liu et al. Art Int Surg. 2025;5:425-33 https://dx.doi.org/10.20517/ais.2025.01

specificity, while the GANomaly framework demonstrated greater sensitivity for detecting mesh infection or
pulmonary failure. Specifically, conventional DLM predicted mesh failure with 96% specificity but only 25%
sensitivity, compared to 78% specificity and 68% sensitivity for the GANomaly framework. Similarly, for
pulmonary failure, conventional DLM achieved 92% specificity vs. 67% for GANomaly, but only 27%
sensitivity vs. 73%. The authors suggested that because GANomaly is trained only on negative cases (no
mesh infection or no pulmonary failure), it is less affected by the limited number of positive outcomes.
However, they concluded that the conventional DLM framework should not be discarded, given its superior
specificity, but rather used alongside the GANomaly method.

While AI has proven useful in imaging evaluation and prediction, several studies have also incorporated
clinical data into predictive models [28-30] . Several risk assessment tools already exist, such as the Ventral
Hernia Risk Score, Hernia Wound Risk Assessment Tool, and NSQIP Surgical Risk Calculator [31-33] .
However, these rely on linear regression models that assume risk factors behave in a linear and additive
[34]
manner . O’Brien et al. applied a machine learning model to predict surgical site infection using clinical
data extracted from medical records , achieving accurate discrimination between infection and non-
[28]
infection. Hassan et al. demonstrated that machine learning algorithms based on preoperative clinical data -
such as patient comorbidities and medical history - effectively predicted recurrence, surgical site infections,
[29]
and readmission . Wilson et al. evaluated whether adding clinical data to DLMs improved the prediction
[30]
of hernia recurrence . Their results showed that models trained solely on clinical data (including age, sex,
BMI, diabetes status, and tobacco use history) outperformed those trained on images only or on images
combined with clinical data. This finding is notable, given that prior studies predicting initial hernia
occurrence primarily relied on imaging data alone [17,21] .

Chat functions and patient education
The utility of AI chatbots for patient counseling and education has also been a topic of investigation. Li et al.
evaluated ChatGPT’s ability to answer common patient questions regarding abdominoplasty . They
[35]
provided the chatbot with a list of questions from the American Society of Plastic Surgeons related to
abdominoplasty. Although the chatbot could answer frequently asked questions, some responses were
outdated and lacked personalized recommendations. Lim et al. extended this work by comparing four
popular chatbots - OpenAI’s ChatGPT, Anthropic’s Claude, Google Gemini, and Bing CoPilot . They
[36]
found that while all platforms delivered instant and easily accessible responses, there was variability in
language quality and supplementary information; similarly, none could offer personalized advice. Lima et al.
[37]
also assessed the appropriateness of ChatGPT’s responses to questions about ventral hernia repair . While
chatbots may serve as valuable initial tools for patient education, they should not replace surgical
consultations.

Alternative uses and future directions of AI
The application of AI has been rapidly expanding across medical specialties, with the most frequent use in
diagnostic fields . Machine learning, AI, and large language models have been used to tailor cardiac
[38]
treatments and assist in clinical decision making. DLMs have been applied in precision medicine to predict
the risk of coronary artery disease by analyzing risk factors such as smoking, diabetes, hypertriglyceridemia,
and correlating these with electrocardiogram (EKG) or echocardiography findings . Additionally,
[9]
integrating histopathology, genetic risk factors, and imaging modalities - such as cardiac MRI,
echocardiography, and coronary angiography - has improved prediction of hospital admissions due to
cardiac complications, including acute decompensated heart failure or stroke .
[8]