Enabling a new model for healthcare with AI co-clinician

AI co-clinician: researching the path toward AI-augmented care — Google DeepMind Skip to main content

April 30, 2026 Science Enabling a new model for healthcare with AI co-clinician Alan Karthikesalingam, Vivek Natarajan and Pushmeet Kohli

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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 . 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. 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.

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

Augmenting clinicians with AI co-clinician 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. 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). In head-to-head blind evaluations, physicians consistently preferred AI co-clinician’s responses to leading evidence synthesis tools. In objective analysis of 98 realistic primary care queries, our system recorded zero critical errors in 97 cases, improving over two AI systems widely used by physicians.

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.

Beyond reliable synthesis of clinical evidence, AI systems should answer queries about medications and therapeutic interventions with the precision that doctors demand. This is a difficult task for AI yet remains underexplored. To address this gap, we evaluated AI co-clinician on the OpenFDA set of RxQA questions, a challenging benchmark designed to assess complex medication knowledge and reasoning. We saw significant progress in navigating these tests, surpassing other frontier AI systems especially when questions were posed in the open-ended way they’re asked in real care. The findings underscore the potential for advanced AI to provide helpful assistance as clinicians navigate the increasingly data-intensive requirements of care planning and management.

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.

Researching AI co-clinician’s real time multimodal capabilities in telemedical settings Beyond assistive clinician-facing settings, we are also investigating how AI co-clinician performs within patient-facing research contexts. Expert clinical assessment traditionally includes subtle visual and auditory cues, such as observing a patient’s gait, the nuances of respiratory patterns, or the appearance of skin changes. While prior studies (including our work with Beth Israel Deaconess Medical Center ) demonstrated value in AI text-chats before a doctor’s appointment, restricting interactions to text fundamentally constrains the clinical value of AI. Medicine isn’t just text; it requires eyes, ears and a voice. 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 ” Working with academic physicians at Harvard and Stanford, we designed a randomized simulation study with 20 synthetic clinical scenarios and 10…