Year: 2025
Type: conference-paper
Abstract: 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.
Institutions: National University of Singapore, University College Dublin
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Awards: G1493808341, G4419537567
OpenAlex ID: W4412035812