Behavior Change Content and Implementation of Large Language Model–Driven Conversational Agents in Cardiometabolic Care: Scoping Review - Summary - MDSpire
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Behavior Change Content and Implementation of Large Language Model–Driven Conversational Agents in Cardiometabolic Care: Scoping Review
To provide a structured overview of patient-facing LLM-driven conversational agents designed for cardiometabolic care, with the aim of mapping the landscape of existing literature on behavior change techniques (BCTs), their delivery methods, transparency of reporting, and user experience.
Approach:
Scoping Review Methodology: The review followed a five-stage methodological framework to synthesize studies on LLM-driven conversational agents for cardiometabolic care.
Key Findings:
The literature on LLM-driven conversational agents in cardiometabolic care is heterogeneous and fragmented.
There is variability in how studies report the underlying models, prompts, and implementation details.
The review aims to clarify the BCTs used in these agents and their operationalization.
Interpretation:
The scoping review aims to summarize the current state of LLM-driven interventions in cardiometabolic care.
Limitations:
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Conclusion:
The review provides insights into the integration of LLM-driven agents into cardiometabolic care pathways.
National survey findings suggest many US adults report making health decisions based on social media despite widespread concerns about the accuracy of health information shared to the platforms.
