The ability to find clear, relevant, and personalized health information is a cornerstone of empowerment for medical patients. Yet, navigating the world of online health information is often a confusing, overwhelming, and impersonal experience. We are met with a flood of generic information that does not account for our unique context, and it can be difficult to know what details are relevant.
Large language models (LLMs) have the potential to make this information more accessible and tailored. However, many AI tools today act as passive “question-answerers” — they provide a single, comprehensive answer to an initial query. But this isn’t how an expert, like a doctor, helps someone navigate a complex topic. A health professional doesn’t just provide a lecture; they ask clarifying questions to understand the full picture, discover a person’s goals, and guide them through the information maze. Though this context-seeking is critical, it’s a significant design challenge for AI.
In “Towards Better Health Conversations: The Benefits of Context-Seeking”, we describe how we designed and tested our “Wayfinding AI”, an early-stage research prototype, based on Gemini, that explores a new approach. Our fundamental thesis is that by proactively asking clarifying questions, an AI agent can better discover a user’s needs, guide them in articulating their concerns, and provide more helpful, tailored information. In a series of four mixed-method user experience studies with a total of 163 participants, we examined how people interact with AI for their health questions, and we iteratively designed an agent that users found to be significantly more helpful, relevant, and tailored to their needs than a baseline AI agent.
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