Explainable AI framework for psilocybin depression treatment optimization

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.

Study at a glance

Abstract

Introduction This computational modeling study introduces a novel Explainable Artificial Intelligence (XAI) framework for optimizing single-dose psilocybin treatment protocols through personalized intervention modeling using publicly available mental health datasets. All results presented are derived from novel simulated data and predictive modeling only, not from real-time clinical implementations or actual patient treatments. Methods The mathematical optimization model integrates digital twin technologies, multimodal depression detection systems, and Bayesian optimization algorithms to create comprehensive computational patient profiles with temporal resolution processing capabilities at 250 Hz sampling frequency. Validation employed three publicly available datasets: (1) the Psilocybin Precision Functional Mapping dataset from OpenNeuro containing neuroimaging data from 7 participants, (2) the MODMA multimodal mental disorder dataset with 53 participants including electroencephalography and audio signals, and (3) a meta-analytic psilocybin therapy outcomes dataset containing aggregated results from 10 clinical trials. The framework incorporates pharmacokinetic modeling with an absorption rate constant of 0.45 per hour and an elimination rate constant of 0.23 per hour, receptor occupancy dynamics based on a dissociation constant of 6.3 nanomolar, and simulated real-time monitoring protocols processing physiological parameters including heart rate variability, blood pressure measurements, and cortisol levels at a 1 Hz frequency. Results The simulated computational model demonstrates significant improvements in prediction accuracy, reaching 94.7%, and therapeutic transparency, achieving 89.3% explainability scores. Simulated validation demonstrates computational precision of 92.8% in predicting treatment response patterns across diverse patient populations in silico . The proposed computational methodology addresses key challenges in psychedelic-assisted therapy modeling through interpretable artificial intelligence models, achieving 96.2% computational safety index scores and 91.5% algorithmic compliance metrics in simulation environments. Energy-efficient computational architecture achieves 73.4% carbon footprint reduction through optimized algorithm design and sparse matrix representations. Discussion This study presents a theoretical computational framework for modeling therapeutic outcomes through simulation and prediction, establishing a foundation for future clinical validation through prospective randomized controlled trials. The framework supports sustainable digital mental healthcare delivery systems compatible with renewable energy infrastructure. All findings represent computational predictions and simulated scenarios requiring extensive clinical validation before any practical application.