Towards better health conversations: Research insights on a “wayfinding” AI agent based on Gemini (opens in new tab)

Google Research has developed "Wayfinding AI," a research prototype based on Gemini designed to transform health information seeking from a passive query-response model into a proactive, context-seeking dialogue. By prioritizing clarifying questions and iterative guidance, the agent addresses the common struggle users face when attempting to articulate complex or ambiguous medical concerns. User studies indicate that this proactive approach results in health information that participants find significantly more helpful, relevant, and tailored to their specific needs than traditional AI responses.

Challenges in Digital Health Navigation

• Formative research involving 33 participants highlighted that users often struggle to articulate health concerns because they lack the clinical background to know which details are medically relevant.
• The study found that users typically "throw words" at a search engine and sift through generic, impersonal results that do not account for their unique context.
• Initial UX testing revealed a strong user preference for a "deferred-answer" approach, where the AI mimics a medical professional by asking clarifying questions before jumping to a conclusion.

Core Design Principles of Wayfinding AI

• Proactive Conversational Guidance: At every turn, the agent asks up to three targeted questions to reduce ambiguity and help users systematically share their "health story."
• Best-Effort Answers: To ensure immediate utility, the AI provides the best possible information based on the data available at that moment, while noting that the answer will improve as the user provides more context.
• Transparent Reasoning: The system explicitly explains how the user’s most recent answers have helped refine the previous response, making the AI’s internal logic understandable.

Split-Stream User Interface

• To prevent clarifying questions from being buried in long paragraphs, the prototype uses a two-column layout.
• The left column is dedicated to the interactive chat and specific follow-up questions to keep the user focused on the dialogue.
• The right column displays the "best information so far" and detailed explanations, allowing users to dive into the technical content only when they feel enough context has been established.

Comparative Evaluation and Performance

• A randomized study with 130 participants compared the Wayfinding AI against a baseline Gemini 2.5 Flash model.
• Participants interacted with both models for at least three minutes regarding a personal health question and rated them across six dimensions: helpfulness, question relevance, tailoring, goal understanding, ease of use, and efficiency.
• The proactive agent outperformed the baseline significantly, with participants reporting that the context-seeking behavior felt more professional and increased their confidence in the AI's suggestions.

The research suggests that for sensitive and complex topics like health, AI should move beyond being a passive knowledge base. By adopting a "wayfinding" strategy that guides users through their own information needs, AI agents can provide more personalized and empowering experiences that better mirror expert human consultation.