Effectiveness of ChatGPT in explaining complex medical reports to patients

Mengxuan Sun, Ehud Reiter, Anne E Kiltie, George Ramsay, Lisa Duncan, Peter Murchie, Rosalind Adam
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Abstract

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.
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ChatGPT 向患者解释复杂医疗报告的有效性
电子健康记录包含有关患者医疗状况的详细信息,但即使患者能够获得这些信息,他们也很难理解。我们探讨了 ChatGPT(GPT 4)能否帮助向结肠直肠癌和前列腺癌患者解释多学科小组(MDT)报告。这些报告是用密集的医学语言撰写的,并假定了临床知识,因此可以很好地测试 ChatGPT 向患者解释复杂医学报告的能力。我们请临床医生和非专业人士(非患者)审查 ChatGPT 的解释和回复。我们还组织了三个焦点小组(包括癌症患者、护理人员、计算机科学家和临床医生)讨论 ChatGPT 的输出结果。我们的研究强调了信息不准确、语言不当、个性化有限、人工智能不信任以及将大型语言模型(LLM)整合到临床工作流程中的挑战等问题。在使用 LLM 向患者解释复杂的个人医疗信息之前,这些问题都需要得到解决。
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