A Virtual Patients Ensemble Approach for Predicting Surgical Complications

AI has shown promise in predicting surgical complications, but most existing models estimate overall risk levels rather than identifying the specific complications an individual patient may develop. We present an AI agent that uses a Virtual Patients Ensemble (VPE) approach to generate individualized predictions of surgical complications from unstructured case descriptions. The agent applies structured reasoning to extract diagnoses, surgical procedures, and risk factors from clinical narratives. From this profile, it generates a cohort of N virtual patients, each a plausible variation of the original case. This ensemble captures uncertainty in patient-specific risk factors and grounds LLM-based clinical reasoning in individualized clinical scenarios. For each virtual patient, the agent predicts the most likely complications, and a final distribution is presented over the virtual patients. The agent was evaluated on 1440 case reports from the PMC-Patients dataset, of which 186 met the inclusion criteria. Predictive performance was compared with both null-hypothesis expectations and baseline LLM predictions. The agent correctly identified 32% of the observed complications, significantly outperforming the null-hypothesis baseline and a baseline prediction generated by the LLM. Unlike risk calculators or machine-learning models trained on population averages, this approach derives predictions directly from a patients clinical profile, generating a VPE to predict specific complications rather than general risk levels. The results suggest that ensemble-based, patient-centered simulation can support clinical decision-making by offering interpretable, individualized predictions. Prospective validation is required before integration into practice. We thus provide surgeons with an app for experimenting with the agent and providing feedback for improvement.