People over trust AI-generated medical responses and view them to be as valid as doctors, despite low accuracy
Not on the map. Why?
Abstract (arXiv)
This paper presents a comprehensive analysis of how AI-generated medical responses are perceived and evaluated by non-experts. A total of 300 participants gave evaluations for medical responses that were either written by a medical doctor on an online healthcare platform, or generated by a large language model and labeled by physicians as having high or low accuracy. Results showed that participants could not effectively distinguish between AI-generated 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 similar to Doctors' responses, if not more. 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 if not more to the reaction they displayed towards doctors' responses. This increased trust placed on inaccurate or inappropriate AI-generated medical advice can lead to misdiagnosis and harmful consequences for individuals seeking help. Further, participants were more trusting of High Accuracy AI-generated responses when told they were given by a doctor and experts rated AI-generated responses significantly higher when the source of the response was unknown. Both experts and non-experts 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. Ensuring AI systems are implemented with medical professionals should be the future of using AI for the delivery of medical advice.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 10 of its 48 references that are in the map. First posted on arXiv 11 Aug 2024.
Builds on
10 of 48 references.
References · 10
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Built on by
Citing papers per year: 2025: 3; 2026: 4.
7 papers.
- 2025Radiology's Last Exam (RadLE): Benchmarking Frontier Multimodal AI Against Human Experts and a Taxonomy of Visual Reasoning Errors in Radiologycited by 2
- 2026The Bots of Persuasion: Examining How Conversational Agents' Linguistic Expressions of Personality Affect User Perceptions and Decisionscited by 2
- 2025A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Modelscited by 1
- 2025Safety boundary maintenance in consumer AI systems responding to pediatric health queries: a cross-platform benchmark evaluation under naturalistic and adversarially pressured conditionscited by 0
- 2026What Makes a Good Doctor Response? A Study on Text-Based Telemedicinecited by 0
- 2026Political Plasticity: An Analysis of Ideological Adaptability in Large Language Modelscited by 0
- 2026Available but Unclaimed: An Empirical Study of Human-AI Synergycited by 0
- 2026Available but Unclaimed: An Empirical Study of Human-AI Synergy15 Sep 2026
- 2026Political Plasticity: An Analysis of Ideological Adaptability in Large Language Models8 May 2026
- 2026What Makes a Good Doctor Response? A Study on Text-Based Telemedicine19 Feb 2026
- 2026The Bots of Persuasion: Examining How Conversational Agents' Linguistic Expressions of Personality Affect User Perceptions and Decisions19 Feb 2026
- 2025Safety boundary maintenance in consumer AI systems responding to pediatric health queries: a cross-platform benchmark evaluation under naturalistic and adversarially pressured conditions26 Dec 2025
- 2025Radiology's Last Exam (RadLE): Benchmarking Frontier Multimodal AI Against Human Experts and a Taxonomy of Visual Reasoning Errors in Radiology29 Sep 2025
- 2025A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models8 Jul 2025