The power to seek out clear, related, and customized well being info is a cornerstone of empowerment for medical sufferers. But, navigating the world of on-line well being info is commonly a complicated, overwhelming, and impersonal expertise. We’re met with a flood of generic info 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 info extra accessible and tailor-made. Nevertheless, many AI instruments at this time act as passive “question-answerers” — they supply a single, complete reply to an preliminary question. However this is not how an professional, like a health care provider, helps somebody navigate a posh subject. A well being skilled would not simply present a lecture; they ask clarifying questions to grasp the complete image, uncover an individual’s objectives, and information them via the data maze. Although this context-seeking is vital, it is a important design problem for AI.
In “In direction of Higher Well being Conversations: The Advantages of Context-In search of”, 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 consumer’s wants, information them in articulating their considerations, and supply extra useful, tailor-made info. In a collection of 4 mixed-method consumer expertise research with a complete of 163 individuals, we examined how individuals 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.
