# Putting ChatGPT's Medical Advice to the (Turing) Test

arXiv: https://arxiv.org/abs/2301.10035 · HTML: https://mlsanity.com/paper/2301.10035/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2023.

## Built on by (most cited first, 24 of 24)

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- [A New Dialogue Response Generation Agent for Large Language Models by Asking Questions to Detect User's Intentions](https://mlsanity.com/paper/2310.03293/) (2023)
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- [Protected group bias and stereotypes in Large Language Models](https://mlsanity.com/paper/2403.14727/) (2024)
- [POSQA: Probe the World Models of LLMs with Size Comparisons](https://mlsanity.com/paper/2310.13394/) (2023)
- [Knowledge Sharing in Manufacturing using Large Language Models: User Evaluation and Model Benchmarking](https://mlsanity.com/paper/2401.05200/) (2024)
- [Evaluating ChatGPT on Medical Information Extraction Tasks: Performance, Explainability and Beyond](https://mlsanity.com/paper/2601.21767/) (2026)
- [A Survey on the Real Power of ChatGPT](https://mlsanity.com/paper/2405.00704/) (2024)

## Abstract (arXiv)

Objective: Assess the feasibility of using ChatGPT or a similar AI-based chatbot for patient-provider communication. Participants: A US representative sample of 430 study participants aged 18 and above. 53.2% of respondents analyzed were women; their average age was 47.1. Exposure: Ten representative non-administrative patient-provider interactions were extracted from the EHR. Patients' questions were placed in ChatGPT with a request for the chatbot to respond using approximately the same word count as the human provider's response. In the survey, each patient's question was followed by a provider- or ChatGPT-generated response. Participants were informed that five responses were provider-generated and five were chatbot-generated. Participants were asked, and incentivized financially, to correctly identify the response source. Participants were also asked about their trust in chatbots' functions in patient-provider communication, using a Likert scale of 1-5. Results: The correct classification of responses ranged between 49.0% to 85.7% for different questions. On average, chatbot responses were correctly identified 65.5% of the time, and provider responses were correctly distinguished 65.1% of the time. On average, responses toward patients' trust in chatbots' functions were weakly positive (mean Likert score: 3.4), with lower trust as the health-related complexity of the task in questions increased. Conclusions: ChatGPT responses to patient questions were weakly distinguishable from provider responses. Laypeople appear to trust the use of chatbots to answer lower risk health questions.