The Benefits and Risks of LLMs for Facilitating Medical Decision-Making Among Laypersons

作者：Charisse Foo, Pin Sym Foong, Camille Nadal, Natasha Ureyang, Thant Naylin, Gerald Choon‐Huat Koh · 年份：2025 · DOI：10.1145/3715336.3735779 · 被引用次数：3 · 研究领域：Artificial Intelligence in Healthcare and Education、Electronic Health Records Systems、Ethics in Clinical Research

We explored the potential of Large Language Models (LLMs) to facilitate laypersons' selection of treatment goals within a complex medical decision-making context.Using ChatGPT-4o, we developed an LLM-enhanced tool to guide users through goal elicitation, clarification, and revision.Our findings demonstrate that LLM features can effectively support these key aspects of decision-making.However, the absence of human interaction, the lack of patientand context-specific treatment information, and the risk of information overload due to unconstrained access to LLM-generated content present significant risks.To balance the benefits and risks, we propose that LLM-enhanced facilitation tools for asynchronous, independent use should be clinician-initiated, constrain broad information search, and focus on creating a safe space for the exploration of laypersons' preferences and goals regarding the difficult challenges in balancing treatment and tradeoffs for quality of life.