Effectiveness of ChatGPT in explaining complex medical reports to patients
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Abstract (arXiv)
Electronic health records contain detailed information about the medical condition of patients, but they are difficult for patients to understand even if they have access to them. We explore whether ChatGPT (GPT 4) can help explain multidisciplinary team (MDT) reports to colorectal and prostate cancer patients. These reports are written in dense medical language and assume clinical knowledge, so they are a good test of the ability of ChatGPT to explain complex medical reports to patients. We asked clinicians and lay people (not patients) to review explanations and responses of ChatGPT. We also ran three focus groups (including cancer patients, caregivers, computer scientists, and clinicians) to discuss output of ChatGPT. Our studies highlighted issues with inaccurate information, inappropriate language, limited personalization, AI distrust, and challenges integrating large language models (LLMs) into clinical workflow. These issues will need to be resolved before LLMs can be used to explain complex personal medical information to patients.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 29 references that are in the map. First posted on arXiv 23 Jun 2024.
Builds on
5 of 29 references.
References · 5
- 2024Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMscited by 229
- 2022ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reportscited by 101
- 2023Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinationscited by 63
- 2023Zero-shot information extraction from radiological reports using ChatGPTcited by 15
- 2022User-Driven Research of Medical Note Generation Softwarecited by 13
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Citing papers per year: 2025: 2.
2 papers.