Introduction to Predictive Analytics in Psychedelic Medicine The resurgence of psychedelic-assisted therapy has ushered in a new era of psychiatric care, promising significant breakthroughs for treatment-resistant depression, post-traumatic stress disorder, and anxiety. As these substances move toward clinical integration, the challenge of patient stratification remains paramount. Because subjective experiences and clinical outcomes vary significantly across individuals, the ability to forecast how a specific patient will respond to a session is critical for safety and efficacy. Machine learning serves as the primary engine for this predictive modeling, offering the computational power to synthesize vast amounts of biological and psychological data into actionable clinical insights. By leveraging sophisticated algorithms, researchers are beginning to transition from generalized treatment protocols to personalized therapeutic trajectories. This shift is not merely academic; it...