Google Research
Google Research is partnering with Included Health to launch a first-of-its-kind nationwide randomized study evaluating conversational AI in real-world virtual care workflows, aiming to gather rigorous prospective evidence on AI performance in clinical settings.
AI 生成摘要
Google Research 與 Included Health 合作，將啟動一項首創的全國性隨機對照研究，評估對話式 AI 在真實世界虛擬照護工作流程中的表現，旨在收集 AI 在臨床環境中表現的嚴謹前瞻性證據。
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February 3, 2026
Mike Schaekermann and Cameron Chen, Research Leads
In partnership with Included Health, we will be launching a first-of-its-kind nationwide study to evaluate conversational AI within real-world virtual care workflows. This research will move beyond simulation and retrospective data and aim to gather rigorous prospective evidence on how AI performs in clinical settings at scale.
AI systems capable of clinical reasoning and dialogue have the potential to dramatically increase access to medical expertise and care while giving physicians back time with their patients where it truly matters. However, developing these technologies responsibly requires a rigorous, evidence-based approach. Over the past few years, our teams have explored the "art of the possible" through research systems that demonstrate clinician-level capabilities in simulated settings. While we have begun testing the safety and feasibility of these systems in clinical settings, moving to the next stage of assessing these systems requires additional rigor and scale. It involves studying the utility and impact of AI in virtual care involving more patients across an array of geographies and conditions and with controlled comparisons.
Today, we are announcing a significant step in that ongoing research journey: In partnership with Included Health, a leading US healthcare provider, we will be launching, pending Institutional Review Board (IRB) approval, a prospective consented nationwide randomized study to assess AI in a real-world virtual care setting. This new research will build upon our foundational research on the use of AI for diagnostic and management reasoning, personalized health insights and navigating health information.
This work represents a significant evolution in our research. Early studies published in Nature first assessed our AI system’s diagnostic reasoning capabilities, including its assistive effect for physicians. We then compared the system’s conversational diagnostic capabilities to those of primary care physicians in simulated settings with patient actors. In addition to understanding capabilities, we also explored a physician-centered paradigm with asynchronous oversight of AI. Our initial step toward testing conversational AI in real-world clinical settings was a single-center feasibility study in partnership with Beth Israel Deaconess Medical Center. The study’s goal was to demonstrate the system’s safety based on outcome measures like the number of interruptions by the safety supervisor in response to safety concerns. We have observed strong indications of safety in this initial study and look forward to sharing results when complete.
Google is partnering with Included Health, a leading US healthcare provider on a nationwide randomized study to assess AI in real-world virtual care settings.
Our new study will move beyond feasibility to use a randomized controlled trial setup with consented participants recruited nationwide. By gathering robust evidence at this scale, we aim to better understand the capabilities and limitations of our AI for managing patient interactions in real-world virtual care workflows compared to standard clinical practice, for real patients and concerns.
We take a responsible approach to characterizing the helpfulness and safety of conversational medical AI.
This phased approach to studying conversational AI in health ensures that as the stages of research proceed, more data becomes available about the patient and clinician experience, safety and usefulness of the AI system which will guide subsequent innovation responsibly. We believe that a responsible approach to conversational AI in health settings should adopt high standards of evidence generation, similar to other interventions in medicine. This is a crucial step towards ensuring that AI can be deployed safely in healthcare while building trust with patients and care teams.
This study is informed by years of foundational research across Google, where we have systematically investigated the capabilities required for a helpful and safe medical AI.
We began by tackling the core challenge of the medical interview with AMIE. Our research with patient actors and synthetic clinical scenarios demonstrated that an AI system trained with simulated self-play could match or exceed primary care physicians in diagnostic accuracy and conversation quality during simulated consultations. We further advanced these capabilities to support longitudinal disease management, equipping the system to reason over clinical guidelines and patient history to plan investigations and treatments, as well as reasoning through multimodal evidence.
Recognizing that health extends beyond the clinic, we also investigated how AI can reason over personal health data through our retrospective research on the Personal Health Agent (PHA). This research explored how multimodal models could analyze sleep patterns and activity metrics from wearables to provide personalized coaching and insights. By using a collaborative multi-agent architecture, our PHA demonstrated how AI can act as a data scientist, domain expert, and health coach all in one, capabilities that are essential for understanding a patient's full health context. These insights also informed experiments in Fitbit Labs, such as the Symptom Checker and Medical Records Navigator and Plan for Care, which help us understand how users access personalized support when assessing symptoms at home and preparing for an upcoming doctor’s visit.
To support people in their search for health information online, we demonstrated how a novel “wayfinding” AI agent helps people find better information through proactive conversational guidance, goal understanding, and tailored conversations. This stream of research has provided critical insights into how to structure AI interactions that are clear, helpful, and grounded in the practical realities of a health journey.
These distinct threads of research — diagnostic and management reasoning, personal health insights, and navigating health information — have laid the groundwork for the AI system we are examining in this and future studies. By moving from demonstrating the art of the possible in the lab to studying AI systems at scale in the real world, we are taking a critical step toward making high-quality care accessible to everyone through frontier models of medical intelligence.
Google has conducted foundational research on conversational AI in various domains including diagnostic and management reasoning (AMIE), personalized health insights (PHA), and navigating health information (Wayfinding AI).
The upcoming launch of this nationwide randomized study, in partnership with Included Health, will mark a significant step forward in the assessment of our conversational AI in healthcare. By moving from simulated settings and small-scale feasibility studies to this large-scale, real-world nationwide randomized study, we are establishing a new, high standard of evidence generation for medical AI. Our goal is to rigorously understand how AI systems, drawing from foundational research like AMIE, PHA, and Wayfinding AI, can be safe and helpful in virtual care workflows for real patients and concerns. This phased, evidence-based approach is essential for ensuring that high-quality, AI-powered care can be developed safely and responsibly to increase access to medical expertise for everyone.
We are grateful for the partnership with Included Health. The study is a joint effort across many teams at Google, including Google Research, Google DeepMind, Google Platforms and Devices, and Google for Health.
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