Stanford University
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Eric Lin
Clinical Assistant Professor (Affiliated), Psych/General Psychiatry and Psychology (Adult)
Staff, Psychiatry and Behavioral SciencesBioEric Lin, MD, is a Clinical Assistant Professor (Affiliated) of Psychiatry and Behavioral Sciences at Stanford University School of Medicine and an addiction psychiatrist at VA Palo Alto. His academic work focuses on the intersection of artificial intelligence, large language models, machine learning, and psychiatry, with particular interest in the clinical evaluation, safety, and governance of AI systems used in mental health contexts.
Dr. Lin’s work examines how AI systems should be evaluated when they interact with patients, clinicians, or psychologically vulnerable users. He is especially interested in the limitations of benchmark-driven evaluation, the role of psychiatric expertise in AI safety assessment, and the development of clinically meaningful frameworks for evaluating mental health chatbots, digital therapeutics, AI-enabled clinical tools, and emotionally responsive AI systems. His recent work includes projects on LLM behavior in mental health contexts, clinical AI red-teaming, AI-enabled medical device policy, clinical natural language processing, and computational phenotyping in psychiatry.
His broader intellectual interests include psychopathology, personality assessment, psychoanalytic and psychodynamic models of mind, and the challenge of translating complex clinical judgment into rigorous evaluation frameworks for AI systems. He is particularly interested in how psychiatric concepts such as risk, vulnerability, therapeutic interaction, delusional thinking, emotional dependence, and personality structure can inform the evaluation and governance of AI systems in mental health.
Dr. Lin completed psychiatry residency at Yale University, where he trained in the Neuroscience Research Training Program, and later completed a medical informatics fellowship through VA Boston. In the fellowship, he conducted research at Harvard Medical School/McLean Hospital on computational and digital approaches to psychiatric phenotyping, including clinical natural language processing, machine learning, and biostatistical methods. He is board certified in psychiatry and clinical informatics.
His clinical and teaching work in addiction psychiatry informs his broader interest in psychiatric complexity, risk assessment, care navigation, and real-world implementation of AI tools in health care. He is interested in collaborations across psychiatry, computer science, human-centered AI, health policy, digital mental health, and responsible technology development.
