Placebo, Nocebo, and Machine Learning: How Generative AI Could Shape Patient Perception in Mental Health Care
Bibliographic record
Abstract
Unlabelled: The emergence of generative artificial intelligence (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 mental health 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 particularly in mental health care, emphasizing the importance of transparency, ethical oversight, and psychosocial awareness as these technologies evolve.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".