Artificial intelligence and psychedelic medicine

• Published in: Annals of the New York Academy of Sciences 2024-10, Vol.1540 (1), p.5-12
• Main Authors: Sarris, Jerome, Halman, Andreas, Urokohara, Anna, Lehrner, Mathew, Perkins, Daniel
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Brain; Cognitive ability; computational psychiatry; Computer applications; Design optimization; disruptive innovation; DMT; Drug development; Ethics; Health services; machine learning; Mental health; natural language processing; Neural networks; Pharmacodynamics; psilocybin; Psychedelic drugs; Security; Virtual reality
• ISSN: 0077-8923; 1749-6632; 1749-6632
• DOI: 10.1111/nyas.15229

Artificial intelligence (AI) and psychedelic medicines are among the most high‐profile evolving disruptive innovations within mental healthcare in recent years. Although AI and psychedelics may not have historically shared any common ground, there exists the potential for these subjects to combine in generating innovative mental health treatment approaches. In order to inform our perspective, we conducted a scoping review of relevant literature up to late August 2024 via PubMed intersecting AI with psychomedical use of psychedelics. Our perspective covers the potential application of AI in psychedelic medicine for: drug discovery and clinical trial optimization (including pharmacodynamics); study design; understanding psychedelic experiences; personalization of treatments; clinical screening, delivery, and follow‐up (potentially delivered via chatbots/apps); application of psychological preparation, integration, and general mental health support; its role in enhancing treatment via brain modulatory devices (including virtual reality and haptic suits); and the consideration of ethical and security safeguards. Challenges include the need for sufficient data protection and security, and a range of necessary ethical protections. Future avenues of exploration could involve directly administering psychedelics (or providing algorithm‐generated effects) to inorganic AI‐interfaced neural networks that may exceed human brain activity (i.e., cognitive capacity) and intelligence.