Ai-Assisted Intraoperative Complication Prediction.

Abstract

Background:The incorporation of Artificial Intelligence (AI) technologies into surgical settings has the potential to radically transform the domain of intraoperative care by providing real-time complication forecasting. However, the effective use of these systems depends on the acceptance, trust, and confidence of healthcare practitioners towards AI technologies and their utility and relevance within the field, even with rapid technological advancements. 

Objectives:Assess the understanding and expectation of healthcare practitioners regarding AI predictive systems for complications and assess their level of awareness while evaluating the reliability and validity of the measurement tools. 

Methods: A quantitative descriptive cross-sectional study was conducted using a structured questionnaire given to 250 participants composed of surgeons, anesthesiologists, operating room (OR) nurses, and biomedical engineers. The survey was comprised of demographic data and 20 Likert-scale questions. The data was analyzed using descriptive statistics, normality tests employing Shapiro–Wilk tests, as well as reliability calculations through Cronbach’s Alpha. 

Results:Moderate to high awareness of AI was noted alongside operative settings. Ethical concerns, poor training frameworks, and lack of system automation fueled barriers to the widespread integration of AI. Patterns of item response were noted alongside weak measurement correlation flagged by Cronbach's Alpha indicating the results fell well below the conventional acceptance threshold. Further examination using Shapiro-Wilk tests flagged rhythmic strain within normality cross p<0.05 thresholds indicating mixed alignment. As a whole, the clash of focus or balance is driven by over-reliance on AI measuring systems which rigid operational perceptions reveal posture dependence dominated by preconceptions crippled by frameworks devoid of result reliance. 

Conclusion: Intraoperative safety and decision-making at the operational level are enhanced with the integration of AI, revolutionizing attention during surgery. Application gaps, ethical limitations, and infrastructural bottlenecks hinder broader adoption. As such, the study demonstrates the AI-guided surgical measurement gap and the need for precise sharp regulation and policy-driven supporting integration pathways through ethical governance that optimized focus integration structural aids.