EXPLAINABLE ARTIFICIAL INTELLIGENCE IN NEUROSURGICAL DECISION-MAKING: ENHANCING PREDICTIVE ACCURACY, INTERPRETABILITY, AND CLINICAL TRUST IN COMPLEX BRAIN PATHOLOGIES

Abstract

The integration of artificial intelligence (AI) into neurosurgical practice has significantly improved diagnostic precision, surgical planning, and outcome prediction. However, the “black-box” nature of many advanced machine learning models has limited their clinical adoption due to a lack of transparency and interpretability. In high-risk domains such as neurosurgery, where decisions directly impact patient survival and neurological function, the need for explainable and trustworthy AI systems is critical.