Implementation and learning curve in AI-assisted fluid management during abdominal oncologic surgery: a retrospective observational study

Abstract Background Intraoperative fluid management during major abdominal oncologic surgery is complex and highly operator-dependent. Assisted Fluid Management (AFM) is an artificial intelligence–based decision support system designed to guide fluid challenges based on real-time Stroke Volume (SV) analysis. However, limited data are available on how AFM is adopted in routine clinical practice and how clinician interaction with the system evolves over time. Methods We conducted a retrospective observational study based on a prospectively maintained institutional database at a high-volume tertiary referral center. Adult patients undergoing major abdominal oncologic surgery with intraoperative AFM monitoring were included. Two consecutive time periods following AFM implementation were compared. Analyses were performed at the fluid-challenge level and focused on patterns of fluid challenge initiation (clinician-initiated vs AFM-suggested), hemodynamic effectiveness (SV response), and bolus characteristics, as markers of system adoption and learning curve. Postoperative clinical outcomes were not assessed. Results Fifty-nine patients were included, accounting for 404 fluid challenges. Over time, clinician-initiated boluses significantly decreased and AFM-suggested fluid challenges increased (p