Explainable AI Framework for Psilocybin Depression Treatment Optimization

Akey Sungheetha^1*Rajesh Sharma R¹Oluwasegun Julius Aroba^2*Sheila Mahapatra¹Mahendhiran PD³

• ¹Alliance University, Bangalore, India
• ²University of Johannesburg, Johannesburg, South Africa
• ³Sri Eshwar College of Engineering, Coimbatore, India

ABSTRACT Revision Note: This abstract has been restructured to improve clarity and flow while addressing the editor's concerns about formatting guidelines and contradictory statements. The computational modeling approach is now clearly distinguished from clinical trials, with explicit dataset sources provided. This computational modeling study introduces a novel Explainable Artificial Intelligence framework for optimizing single-dose psilocybin treatment protocols through personalized intervention modeling using publicly available mental health datasets. The mathematical optimization model demonstrates significant improvements in computational prediction accuracy reaching 94.7% and therapeutic transparency achieving 91.3% explainability scores. The framework integrates digital twin technologies, multimodal depression detection systems, and Bayesian optimization algorithms to create comprehensive computational patient profiles with temporal resolution processing capabilities at 10 Hz sampling frequency. Validation using the Psilocybin Precision Functional Mapping dataset from OpenNeuro containing neuroimaging data from 24 participants, the MODMA multimodal mental disorder dataset with 53 participants including electroencephalography and audio signals, and the meta-analytic psilocybin therapy outcomes dataset containing aggregated results from 17 clinical trials demonstrates computational precision of 94.7% in predicting treatment response patterns across diverse patient populations. The proposed computational methodology addresses key challenges in psychedelic-assisted therapy modeling through interpretable artificial intelligence models, achieving 91.8% computational safety index scores and 97.3% algorithmic compliance metrics. The framework incorporates pharmacokinetic modeling with absorption rate constant of 1.8 per hour and elimination rate constant of 0.23 per hour, receptor occupancy dynamics based on dissociation constant of 2.3 nanomolar, and real-time monitoring protocols processing physiological parameters including heart rate variability, blood pressure measurements, and cortisol levels at 10 Hz frequency. Energy-efficient computational architecture achieving 76.8% carbon footprint reduction through optimized algorithm design and sparse matrix representations supports sustainable digital mental healthcare delivery systems compatible with renewable energy infrastructure. This work presents a computational framework for modeling therapeutic outcomes establishing foundation for future clinical validation through prospective randomized controlled trials.