Ai-Driven Risk Stratification In Bariatric Surgery: Improving Patient Safety And Surgical Precision

Abstract

Background: Recent applications of Artificial Intelligence (AI) in bariatric surgery have made commendable advances in risk stratification and surgical decision-making. However, the technology's direct effect on patient safety and surgical precision is still debated. It was previously established that the application of AI-based risk stratification can reduce complications and improve clinical outcomes.

Methods: A quantitative, cross-sectional study was developed on 273 healthcare professionals, 186 bariatric surgeons, anesthesiologists, nurses, and healthcare administrators. AI adoption, perceived effectiveness, and surgical outcome effects were assessed via a structured questionnaire. Descriptive and inferential statistical techniques (Shapiro-Wilk normality tests, Cronbach's Alpha (reliability analysis), Pearson correlation, and linear regression analysis) were used to assess the association between AI-driven surgical decision-making and patient safety.

Results: The results suggest high-level AI adoption, with a favorable perception among healthcare professionals. However, Shapiro-Wilk tests indicated that the data were not normally distributed, implying that the perception of AI's efficacy was that of a skew. Barnard et al. cloud found that Cronbach’s Alpha (0.0033) also showed poor internal consistency, raising doubts about the reliability of the survey items. Correlation analysis detected no significant relationship (r = -0.038, p > 0.05) between AI-driven surgical decision accuracy and patient safety improvements. Moreover, the predictive value of the AI-driven decision accuracy for patient safety improvements was low in the linear regression analysis (R² = 0.00147).

Conclusion: Although AI can be promising for risk stratification and surgical planning, little direct evidence exists to show that AI has a significant impact on patient safety. The results indicate that AI should be approached as an adjunctive aid, and not a replacement, whose utility is contingent on the surgeon’s skills, known clinical protocols, and patient-related information. Future research should investigate multifactorial models that integrate other variables within the analysis to maximize our understanding of the role of AI in surgical precision and patient safety. AI has great potential in several

areas of bariatric surgery and with improvements in validation and AI training, the benefits may be maximized.