Ai-Powered Predictive Analytics In General Surgery: Improving Patient Safety And Surgical Outcomes

• DOI: 10.63682/jns.v14i13s.3388
• Publication Date: 2025-04-29T04:57:36Z
• Authors: Sahil Kumar; Syed Ali Mehsam; Tariq Rafique; Muskan Muskan; Shazia Riaz; U. G. Lashari; E. N. Ganesh
• Keywords: Predictive Analytics

Background: The role of AI-based predictive analytics in general surgery. use artificial intelligence (AI) tools surgical environments is influenced by several factors including perceived usefulness, usability, and trust between professionals. This study focused on the core acceptance intelligence-24 surgery with technology model (TAM). Methods: A cross-sectional quantitative survey was conducted 273 healthcare providers (general surgeons, residents, anesthesiologists, operating room nurses, hospital administrators). questionnaire designed to measure Perceived Usefulness (PU), Ease Use (PEU), Trust, Behavioral Intention (BI) AI. We measured a 5-point Likert scale data analysis performed using descriptive statistics, reliability testing (Cronbach’s Alpha), correlation analysis, multiple regression modeling find relationship TAM AI adoption. Results: results demonstrated that (Trust_Q14) only significant predictor variable when considering behavioral intention (p = 0.023), while usefulness ease did not significantly affect Cronbach’s Alpha score (0.087) low, which means internal consistency instrument should be improved. found low R-squared (0.029), indicates factor drives adoption; other like regulatory policies, ethical considerations, institutional support systems may also very important. Similarly, Shapiro-Wilk normality test substantiated non-normal distribution dataset (all variables: p < 0.05), necessitating alternative analytical methods forthcoming studies. Conclusion: identify as dominant driver adoption contexts provide evidence limitations standalone framework for predicting behavior. indicate commitment transparency, development training programs, setting up frameworks will help build adopt [dimensional] X-ray imaging diagnosis solution. Marked current involved only, incorporating external influencing factors, advanced statistical techniques future research further insights into trends While there are challenges overcome, AI-powered has potential improve patient safety, procedure optimization, decision-making