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People over trust AI-generated medical responses and view them to be as valid as doctors, despite low accuracy

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arxiv 2408.15266 v1 pith:3T75MTQM submitted 2024-08-11 cs.HC cs.AIcs.CY

classification cs.HCcs.AIcs.CY
keywords responsesai-generatedmedicalaccuracydoctorsadvicehighparticipants
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 12 citations worldwide. Full citation record

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    LLM experts in credence goods markets reduce efficiency and consumer surplus unless liability or transparent prosocial objectives operate, and expert delegation with transparent objectives can outperform human-only markets.

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    Medical disclaimers in LLM and VLM outputs declined sharply from 2022 to 2025, dropping from 26.3% to 0.97% for text questions and from 19.6% to 1.05% for images.

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