Accepted for/Published in: JMIR Mental Health
Date Submitted: Jun 6, 2025
Open Peer Review Period: Jun 8, 2025 - Aug 3, 2025
Date Accepted: Jul 14, 2025
(closed for review but you can still tweet)
Placebo, Nocebo, and the Machine: How Generative AI Could Shape Patient Perception in Mental Healthcare
ABSTRACT
The emergence of generative AI (GenAI) in clinical settings - particularly in health documentation and communication - presents a largely unexplored but potentially transformative force in shaping placebo and nocebo effects. These psychosocial phenomena are especially potent in mental health care, where outcomes are closely tied to patients' expectations, perceived provider competence, and empathy. Drawing on conceptual understanding of placebo and nocebo effects and the latest research, this Viewpoint argues that GenAI may amplify these effects, both positive and negative. Through tone, assurance, and even the rapidity of responses, GenAI-generated text - either co-written with clinicians or peers, or fully automated - could influence patient perceptions in ways that clinicians may not currently fully anticipate. When embedded in clinician notes or patient-facing summaries, AI language may strengthen expectancies that underlie placebo effects - or, conversely, heighten nocebo effects through subtle cues, inaccuracies, or potentially via loss of human nuance. This article explores the implications of AI-mediated clinical communication, emphasizing the importance of transparency, ethical oversight, and psychosocial awareness as these technologies evolve.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.