INTEGRATION OF AI IN SURGICAL WORKFLOW OPTIMIZATION: CHALLENGES AND FUTURE DIRECTIONSA NARRATIVE REVIEW

Abstract

Background: The integration of artificial intelligence (AI) into surgical workflows has emerged as a transformative development in modern healthcare. With the growing complexity of surgical procedures and the need for improved precision, efficiency, and outcomes, AI offers tools to support clinical decision-making across the perioperative continuum. From preoperative planning to intraoperative guidance and postoperative outcome prediction, AI has demonstrated promising potential to optimize surgical care. Objective: This narrative review aims to explore how AI technologies are currently being applied in surgical planning, intraoperative navigation, and outcome prediction, while critically examining the barriers to integration and identifying directions for future advancement. Main Discussion Points: The review discusses key thematic areas including AI applications in preoperative imaging and simulation, real-time intraoperative decision support, and predictive analytics for postoperative outcomes. It also highlights methodological limitations in current literature, such as small sample sizes, inconsistent outcome measures, and limited external validity. Challenges related to ethical concerns, data quality, algorithmic transparency, and clinical adoption are also addressed. Conclusion: While AI has shown potential to enhance surgical accuracy, efficiency, and personalization, the current evidence base is limited in scope and quality. Clinicians and researchers must collaborate to develop standardized protocols and conduct rigorous multicenter trials to validate AI tools. Addressing existing research gaps will be crucial for integrating AI safely and effectively into everyday surgical practice.

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