Development and retrospective proof-of-concept simulation of an ai-based perioperative decision-support framework for robot-assisted urological surgery

Artificial intelligence (AI) is increasingly being explored as a decision-support tool in surgical oncology. In robot-assisted urological surgery, outcome variability remains influenced by anatomical complexity, disease heterogeneity, and differences in surgical expertise. This study aimed to develop and retrospectively evaluate an AI-based perioperative decision-support model using historical clinical data within a proof-of-concept simulation framework. A retrospective cohort of 600 patients (200 each undergoing robot-assisted partial nephrectomy [PN], radical cystectomy with urinary diversion [RCUD], and robot-assisted radical prostatectomy [RARP]) was analyzed to characterize perioperative, oncological, and functional outcomes. An AI-based perioperative decision-support pipeline integrating imaging features, clinical variables, surgeon experience, and procedural factors was developed and retrospectively applied to these data to generate simulated predictions under hypothetical AI-assisted decision-making. The model was not implemented in clinical practice and did not influence patient management. Observed clinical outcomes were consistent with those reported in contemporary robotic surgical series across the three procedures. Under the assumptions of the simulation framework—that surgeons would modify perioperative management according to model-generated risk estimates and that such management changes would have effect sizes consistent with those reported in the published literature—the simulated scenarios generated exploratory outcome ranges characterized by lower positive surgical margin and major complication rates, together with possible differences in selected functional outcomes, including renal function preservation after PN and early urinary continence after RARP. Similar exploratory scenarios were generated for recurrence risk following RCUD and RARP. These projections were based on unvalidated modeling assumptions and should be interpreted solely as hypothesis-generating scenarios derived from retrospective computational modeling rather than as estimates of achievable clinical benefit, treatment effect, or evidence of AI effectiveness. This proof-of-concept study describes the development and retrospective simulation of an AI-based perioperative decision-support model for robot-assisted urological surgery. The findings demonstrate the feasibility of generating individualized outcome predictions and support further prospective evaluation of AI-assisted surgical planning. Because the results are derived exclusively from retrospective simulation, they should not be interpreted as evidence of clinical effectiveness. Prospective implementation studies are required to determine the real-world impact of AI-assisted decision support.