Enabling a new model for healthcare with AI co-clinician

Health systems worldwide are striving for better outcomes, lower costs, and an improved experience for both patients and clinicians. However, progress is constrained by a global shortage of clinical experts, with the World Health Organization predicting a shortfall of more than 10 million health workers by 2030.

While AI is often seen as the key to bridging this gap, it has not yet been able to fully meet the needs of clinicians and patients. That's why, today, we are announcing our AI co-clinician research initiative, to explore how AI could better amplify doctors’ expertise and deliver higher quality care to patients.

At Google DeepMind, our journey in medical AI has evolved from mastering examination-style tests of medical knowledge with MedPaLM , to matching physician performance in text-based simulated medical consultations with AMIE , including in real-world feasibility trial settings. We also have a long history of studying how clinicians and AI systems might work together .

The transition and deployment of AI into clinical environments requires uncompromising architectural and operational safeguards. In our research on simulations of patient-facing telemedical conversations, AI co-clinician uses a dual-agent architecture: a "Planner" module continuously monitors the conversation, verifying that the "Talker" agent stays within safe clinical boundaries.

The study used a blind comparison of 98 realistic primary care queries, which were curated from a diverse range of sources and subsequently refined by a panel of attending physicians. This multi-step iterative process involved comprehensive background research and the development of query-specific answer metrics to enable a rigorous professional assessment of clinical accuracy and compliance with best practice guidance. By leveraging this expert-led refinement phase, the methodology allowed for a precise characterization of consensus scenario-specific errors of omission and commission, ensuring that the evaluation reflected the complexities of real-world clinical decision-making.

RxQA was originally posed as a multiple-choice question (MCQ) test in which even primary care physicians scored modestly. While our results show significant improvements for AI systems’ MCQ performance in the openly available (OpenFDA) set of RxQA, clinicians’ needs in the real-world present as open-ended questions rather than a need to identify the correct answer from pre-determined options. On this more realistic clinical task of open-ended question-answering about medications, AI co-clinician outperforms available frontier models. Taken together, these results show that AI can mirror human physician proficiency in such aspects of clinical reasoning with opportunities for further improvement.

Similarly, to meet doctors’ needs AI co-clinician prioritizes clinical-grade evidence, performing verification and citation checking for retrieval. The evaluations we report above were constructed by physicians to mirror a range of their real-world evidence needs, formulating questions from hypothetical scenarios for rigorously evaluating AI’s capabilities.

This serves as the foundation of our AI co-clinician research initiative: AI designed to function as a collaborative member of the care team that interacts with patients under expert clinical supervision. We designed and evaluated AI co-clinician in both clinician and patient-facing settings. Addressing both perspectives is key for AI to enhance the quality, cost, availability and experience of care delivery.

For a physician, a tool is useful only if it is trustworthy and factually grounded. We therefore researched how well AI co-clinician might support clinicians by surfacing high-quality evidence.

This is why we are exploring the potential for real-time multimodal AI as an assistive component of the care team. Building on the capabilities of Gemini and Project Astra , we tested the capabilities of AI co-clinician to use live audio and video to engage with patients, simulating telemedical calls where capable AI could one day support better diagnosis and management under expert supervision. Further details regarding our methodology and results are available in our technical report: “ Towards Conversational Medical AI with Eyes, Ears and a Voice ”

We hypothesize that the next evolution of healthcare delivery will entail “triadic care” where AI agents can help patients in their care journeys under the clinical authority of their physician. Medicine has always been a team sport, and AI agents can bring more teammates onto the field: extending clinicians' reach while ensuring they retain judgment and control.

Advancements in research into medical AI so that they might be more trustworthy and helpful for clinicians in assisting patients.

In collaboration with academic physicians, we adapted the " NOHARM " framework to test our AI for "errors of commission" (incorrect information) and "errors of omission" (failure to surface critical information).