People Overtrust AI-Generated Medical Advice despite Low Accuracy
Abstract
We conducted a study in which a total of 300 participants gave evaluations for medical responses that were either written by a medical doctor on an online health care platform or generated by a large language model and labeled by physicians as having high accuracy or low accuracy. Results showed that participants could not effectively distinguish between AI-generated responses and doctors’ responses and demonstrated a preference for AI-generated responses, rating high-accuracy AI-generated responses as significantly more valid, trustworthy, and complete/satisfactory. Low-accuracy AI-generated responses on average performed very similarly to doctors’ responses. Participants not only found these low-accuracy AI-generated responses to be valid, trustworthy, and complete/satisfactory, but also indicated a high tendency to follow the potentially harmful medical advice and incorrectly seek unnecessary medical attention as a result of the response provided. This problematic reaction was comparable with, if not stronger than, the reaction they displayed toward doctors’ responses. Both experts and nonexperts exhibited bias, finding AI-generated responses to be more thorough and accurate than doctors’ responses but still valuing the involvement of a doctor in the delivery of their medical advice.
Study specs
- Year
- 2025
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
Verify your expertise to join discussion
Create an account and verify your credentials to participate in peer discussions.