Generative AI/LLMs for Plain Language Medical Information for Patients, Caregivers and General Public: Opportunities, Risks and Ethics

Abstract

Generative artificial intelligence (gAI) tools and large language models (LLMs) are gaining popularity among non-specialist audiences (patients, caregivers, and the general public) as a source of plain language medical information. AI-based models have the potential to act as a convenient, customizable and easy-to-access source of information that can improve patients' self-care and health literacy and enable greater engagement with clinicians. However, serious negative outcomes could occur if these tools fail to provide reliable, relevant and understandable medical information. Herein, we review published findings on opportunities and risks associated with such use of gAI/LLMs. We reviewed 44 articles published between January 2023 and July 2024. From the included articles, we find a focus on readability and accuracy; however, only three studies involved actual patients. Responses were reported to be reasonably accurate and sufficiently readable and detailed. The most commonly reported risks were oversimplification, over-generalization, lower accuracy in response to complex questions, and lack of transparency regarding information sources. There are ethical concerns that overreliance/unsupervised reliance on gAI/LLMs could lead to the "humanizing" of these models and pose a risk to patient health equity, inclusiveness and data privacy. For these technologies to be truly transformative, they must become more transparent, have appropriate governance and monitoring, and incorporate feedback from healthcare professionals (HCPs), patients, and other experts. Uptake of these technologies will also need education and awareness among non-specialist audiences around their optimal use as sources of plain language medical information.

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