Explainable AI framework for psilocybin depression treatment optimization
Frontiers in Computer Science March 16, 2026 Akey Sungheetha, R. Rajesh Sharma, Oluwasegun Julius Aroba et al.
A computational model using explainable artificial intelligence can optimize single-dose psilocybin treatment protocols by creating personalized patient simulations. The framework, tested on three public datasets (7 participants from a neuroimaging dataset, 53 from a multimodal mental disorder dataset, and aggregated results from 10 clinical trials), achieved 94.7% prediction accuracy and 89.3% explainability scores in simulated environments. The model also demonstrated 92.8% precision in predicting treatment response patterns and a 73.4% reduction in carbon footprint. These results are entirely from simulated data and require clinical validation before any practical use.