{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T21:30:34Z","timestamp":1780695034270,"version":"3.54.1"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:p>The integration of Artificial Intelligence (AI) and Virtual Reality (VR) has transformed medical education; however, performance assessment in high-stakes fields such as stereotactic neurosurgery remains largely dependent on binary or threshold-based metrics. In procedures such as deep brain stimulation (DBS), where safety margins are below 2 mm, these approaches fail to capture indeterminate behaviors, including hesitation, micro-instability, and unstable trajectories, potentially leading to false-positive competence classifications. This study introduces a Neutrosophic Explainable AI (N-XAI) framework that models surgical performance through three independent dimensions: truth (competence), Indeterminacy (instability\/ambiguity), and Falsity (error). Performance is represented in a two-dimensional precision\u2013stability space and quantified using single-valued neutrosophic sets (SVNS). For theoretical validation, a synthetic dataset comprising 60 simulated surgical attempts distributed across three skill groups (expert, indeterminate, and novice) was generated. Neutrosophic competence scores were computed and analyzed using non-parametric statistical tests. The framework successfully differentiated the three groups and identified indeterminate, high-risk cases that achieved acceptable spatial accuracy but exhibited significant instability\u2014patterns that conventional metrics fail to detect. The proposed N-XAI framework provides a mathematically grounded and interpretable approach for modeling uncertainty in immersive neurosurgical simulation. By explicitly accounting for indeterminacy, it enhances the diagnostic value of VR-based training systems and lays the groundwork for future validation in live stereotactic simulation environments.<\/jats:p>","DOI":"10.3389\/fneur.2026.1766089","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T05:28:02Z","timestamp":1771824482000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A neutrosophic explainable AI framework for modeling uncertainty in immersive stereotactic neurosurgical simulation"],"prefix":"10.3389","volume":"17","author":[{"given":"Jesus Rafael","family":"Hechavarria-Hernandez","sequence":"first","affiliation":[{"name":"Universidad Ecotec","place":["Samborond\u00f3n, 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