Artificial Intelligence-assisted Risk Stratification in Aesthetic Surgery: A Prospective Observational Study.

BACKGROUND

Enhancing patient safety and minimizing complications are critical objectives in aesthetic surgery. In 2021, the author developed and validated an artificial intelligence (AI)-assisted risk stratification model to predict complications and support clinical decision-making. This study aimed to evaluate the clinical impact of surgical risk stratification on patient outcomes.

METHODS

A prospective observational study was conducted from January 2021 to May 2024 to assess 3347 patients, using an AI model. The patients were stratified into high-, moderate-, and low-risk groups and received tailored recommendations. A total of 74 patients proceeded with surgery, and their outcomes were analyzed. Statistical analyses included logistic regression and correlation tests ( < 0.05).

RESULTS

Of the 3347 patients assessed, 18.55% were high-risk, 30.56% were moderate-risk, and 50.88% were low-risk patients. Among the 74 patients who underwent surgery, 7 (9.46%) developed 11 complications, with the high-risk group showing a relative risk of 6.73. Logistic regression confirmed that age and Caprini score were independent risk factors, whereas body mass index and smoking showed no statistical association with complications, likely because of effective preoperative risk mitigation, including weight optimization and smoking cessation protocols enforced by the AI model.

CONCLUSIONS

This study demonstrated that AI-assisted risk stratification effectively identifies risk factors in aesthetic surgery, enabling personalized preoperative recommendations to mitigate complications. AI can enhance patient safety and surgical outcomes by enabling systematic risk stratification. Integrating AI into surgical planning optimizes patient selection and supports its implementation in clinical decision-making.