Collaborating on a nationwide randomized study of AI in real-world virtual care

Google and Included Health plan to launch, pending IRB approval, a nationwide randomized study of conversational AI in real-world virtual care. Unlike prior simulated or small feasibility studies, it will prospectively evaluate AI with consented patients across varied conditions and locations, comparing it with standard clinical practice. The goal is to generate rigorous evidence about safety, usefulness, limitations, and impact on patients and clinicians.

Moving from Simulation to Real-World Evaluation

• Earlier research demonstrated clinician-level capabilities in simulated consultations and retrospective analyses.
• A feasibility study with Beth Israel Deaconess Medical Center began testing conversational AI in clinical workflows, using measures such as safety-supervisor interruptions.
• The new study will advance beyond feasibility through:
  • A randomized controlled design
  • Nationwide recruitment
  • Consented participants
  • Real patients, clinical concerns, and virtual-care workflows
  • Controlled comparison with standard practice

A Phased Approach to Medical AI Research

• Google argues that medical AI should be evaluated with evidence standards similar to other medical interventions.
• Each research phase adds information about:
  • Patient and clinician experiences
  • Safety
  • Usefulness
  • The AI system’s capabilities and limitations
• Results from each stage are intended to guide safer, more responsible development and deployment.

Foundational Research Behind the Study

Diagnostic and Management Reasoning

• The AMIE system was developed to handle medical interviews and clinical reasoning.
• Studies with patient actors and synthetic cases found that AMIE could match or exceed primary care physicians in simulated diagnostic accuracy and conversation quality.
• Later work expanded the system to:
  • Longitudinal disease management
  • Clinical-guideline and patient-history reasoning
  • Investigation and treatment planning
  • Interpretation of multimodal evidence

Personalized Health Insights

• Research on the Personal Health Agent examined how AI could interpret personal health data, including sleep and activity information from wearables.
• Its multi-agent architecture combined the roles of:
  • Data scientist
  • Medical domain expert
  • Health coach
• This work informed Fitbit Labs tools such as Symptom Checker and Medical Records Navigator and Plan for Care.

Navigating Health Information

• Google’s “wayfinding” AI research explored how conversational agents can help people find and understand health information.
• The system uses proactive guidance, goal recognition, and tailored conversations to make health information searches more practical and useful.

Practical Conclusion

The partnership with Included Health represents a transition from demonstrating what medical AI can do in controlled environments to measuring how it performs at scale in actual care. A nationwide randomized trial could provide the evidence needed to determine whether conversational AI can safely improve virtual care and expand access to medical expertise.