The management of complex medical conditions such as brain tumors is costly and requires a large team of medical experts. Medical decision-making for these patients is hampered by the frequent inaccessibility of the experts, inconsistencies in the management of different patients, and the fact that multiple experts often yield multiple opinions. The overall goals of this work were (1) to develop and validate an expert system (XNEO - Expert Neuro-Oncology) to assist the medical team deliver efficient, quality care to patients with brain tumors, and (2) to determine the feasibility of constructing expert systems to enhance teaching, research and patient care in an academic setting. Radiation Oncology residents enjoyed learning by using XNEO and it enabled the attendings to identify Rdecision nodes of uncertaintyS that are driving new clinical research. Importantly, residents using XNEO ordered appropriate ancillary tests for patients and made far fewer incorrect treatment decisions. The potential net effect of using artificial intelligence in academic medicine may be more meaningful learning opportunities for trainees, the discovery of new research opportunities, increased patient and family satisfaction, and decreased probability of medical liability. At a time of important transformations in healthcare, we will discuss how novel expert systems hold promise as tools to reduce medical costs, improve the quality of multi-expert medical care, and advance health care education.