A knowledge-based Bayesian network reveals shared prediction errors with physicians in Gamma Knife radiosurgery

Although Artificial Intelligence (AI) has shown strong predictive performance in medicine, its role in providing cognitive support remains unclear. We developed a knowledge-based expert system using a Bayesian Network (BN) to explicitly model an expert neurosurgeon's clinical reasoning for predicting outcomes of Gamma Knife radiosurgery. The BN structure was constructed from elicited expert knowledge and encoded as a directed acyclic (DAG) graph that represents clinically relevant variables and their interrelationships. Model parameters were estimated using a clinical dataset to predict patient overall survival (OS). In parallel, physicians independently estimated outcomes for the same patients, enabling a direct comparison of predictive performance and error characteristics. The BN captured clinically plausible reasoning patterns and produced less skewed prediction errors than physicians, suggesting partial mitigation of individual cognitive bias. However, both the BN and physicians exhibited a heavy-tailed error distribution, systematically failing to predict patients with unexpectedly long-term survival. This convergence of error patterns indicates that both human and model-based predictions are constrained by unobserved or unmodeled factors, limiting their ability to capture exceptional outcomes. These findings highlight a limitation of predictive paradigms based on current expert knowledge and suggest the need for iterative refinement of knowledge-based models. Incorporating additional expert knowledge and updating the underlying structure may help identify previously unmodeled relationships and reduce unexplained variance. Taken together, our results position knowledge-based BNs not only as interpretable predictive tools, but also as evolving frameworks for integrating clinical knowledge and probing the shared limits of human-AI decision-making, highlighting their potential as computational cognitive neuroprosthetic systems that aim to augment physicians' clinical reasoning under uncertainty.