Interdisciplinary Neurosurgery · Published 2026-06-06 · DOI 10.1016/j.inat.2026.102289
Ahmad Sweid, Issam A. Awad
Background: Artificial intelligence (AI) is transforming clinical practice, but its role in medical education remains underdeveloped. Patient care teaching conferences are rich in reasoning and literature appraisal, yet their educational value is rarely formalized. Objective: To describe a hybrid mentorship-plus-AI workflow for converting raw neurosurgical conference notes into structured study materials and to assess its feasibility as a human-supervised educational process. Methods: We conducted a scoping review (2010–2025) of PubMed, Scopus, and Web of Science, identifying 435 records; 10 studies met inclusion criteria. In parallel, we performed a case study in which neurosurgical conference notes were processed with ChatGPT to generate key learning points, open research questions, and targeted literature. Outputs were reviewed for clarity, accuracy, and alignment with educational goals. Results: The scoping review showed that AI use in medical and health professions education is expanding, particularly in radiology training, simulation, and content generation. In the feasibility case study, ChatGPT transformed raw neurosurgical conference notes into concise, structured draft materials with key learning points, research questions, and curated references. These outputs were subsequently reviewed by senior faculty for clarity, accuracy, and alignment with educational goals. Because learner outcomes and formal operational metrics were not directly measured, educational benefit and workflow impact were not inferred. Conclusions: AI may complement conference-based teaching when used within a structured, human-supervised workflow. A structured, human-supervised generative-AI workflow was feasible for converting de-identified conference notes into draft educational materials while preserving the mentor–learner dynamic. These findings should be interpreted as a process description and feasibility assessment rather than evidence of educational effectiveness.
Abstract from DOAJ. Public domain (CC0 1.0).
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Sweid, A., Awad, I. (2026). Artificial intelligence in medical education: a feasibility case study based on proceedings from a patient care teaching conference. Interdisciplinary Neurosurgery. https://doi.org/10.1016/j.inat.2026.102289