Improving AI-Based Decision Support Systems for Surgical Applications

Abstract

The rapid integration of artificial intelligence (AI) in healthcare has led to significant improvements in surgical planning and intraoperative decision-making. However, decision support systems (DSS) based on AI are still in the developmental phase and require robust, reliable, and interpretable models to ensure high-stakes surgical outcomes. This study presents an innovative approach to enhance AI-based DSS for surgical applications by integrating deep learning algorithms with real-time data analytics and clinical feedback loops. The proposed framework leverages multimodal data inputs—from patient medical histories and imaging modalities to intraoperative sensor data—to produce predictive models that assist surgeons in real-time. Through a comprehensive literature review, statistical performance evaluations, and controlled experiments, the study identifies key performance metrics and challenges associated with current systems. The results demonstrate significant improvements in accuracy and decision-making speed compared to legacy models. Our findings emphasize the potential of AI to reduce surgical complications and improve patient outcomes. The study also discusses ethical implications, regulatory challenges, and the need for interdisciplinary collaboration to further refine and implement these advanced systems in clinical settings.