The power to seek out clear, related, and personalised well being data is a cornerstone of empowerment for medical sufferers. But, navigating the world of on-line well being data is usually a complicated, overwhelming, and impersonal expertise. We’re met with a flood of generic data that doesn’t account for our distinctive context, and it may be troublesome to know what particulars are related.

Giant language fashions (LLMs) have the potential to make this data extra accessible and tailor-made. Nonetheless, many AI instruments immediately act as passive “question-answerers” — they supply a single, complete reply to an preliminary question. However this is not how an knowledgeable, like a health care provider, helps somebody navigate a fancy matter. A well being skilled does not simply present a lecture; they ask clarifying questions to know the complete image, uncover an individual’s targets, and information them by way of the knowledge maze. Although this context-seeking is important, it is a important design problem for AI.

In “In direction of Higher Well being Conversations: The Advantages of Context-Looking for”, we describe how we designed and examined our “Wayfinding AI”, an early-stage analysis prototype, primarily based on Gemini, that explores a brand new strategy. Our basic thesis is that by proactively asking clarifying questions, an AI agent can higher uncover a person’s wants, information them in articulating their considerations, and supply extra useful, tailor-made data. In a sequence of 4 mixed-method person expertise research with a complete of 163 individuals, we examined how folks work together with AI for his or her well being questions, and we iteratively designed an agent that customers discovered to be considerably extra useful, related, and tailor-made to their wants than a baseline AI agent.