Towards Conversational AI for Disease Management

Google's AMIE, an LLM-based agentic system, outperformed primary care physicians in management reasoning and medication prescription in a randomized blinded study of 100 multi-visit clinical scenarios. The system demonstrated non-inferiority to doctors in overall management reasoning and scored better in treatment precision and guideline alignment, marking a step toward conversational AI for disease management.

Abstract While large language models LLMs have shown promise in diagnostic dialogue1, their capabilities for effective management reasoning—including disease progression, therapeutic response, and safe medication prescription—remain under-explored. We advance the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer AMIE 1−3 through a new LLM-based agentic system optimized for multi-visit clinical management and dialogue. To ground its reasoning in authoritative clinical knowledge, AMIE leverages Gemini’s long-context capabilities4, combining in-context retrieval with structured reasoning to align its output with up-to-date clinical practice guidelines and drug formularies. In a randomized, blinded virtual Objective Structured Clinical Examination OSCE study, AMIE was compared to 21 primary care physicians PCPs across 100 multi-visit case scenarios designed to reflect UK NICE Guidance and BMJ Best Practice guidelines. AMIE was non-inferior to PCPs in management reasoning as assessed by specialists and scored better in both preciseness of treatments and investigations, and in its alignment with and grounding in clinical guidelines. To benchmark medication reasoning, we developed RxQA, a multiple-choice question benchmark derived from two national drug formularies US, UK and validated by board-certified pharmacists. Though AMIE and PCPs both benefited from the ability to access external drug information, AMIE outperformed PCPs on higher difficulty questions. While further research would be needed before real-world translation, AMIE’s strong performance across evaluations marks a significant step towards conversational AI as a tool in disease management. This is a preview of subscription content, access via your institution https://wayf.springernature.com?redirect uri=https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41586-026-10764-5 Access options Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription 27,99 € / 30 days cancel any time Subscribe to this journal Receive 52 print issues and online access 199,00 € per year only 3,83 € per issue Rent or buy this article Prices vary by article type from$1.95 to$39.95 Prices may be subject to local taxes which are calculated during checkout Author information Authors and Affiliations Corresponding authors Supplementary information Supplementary Information download PDF https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-026-10764-5/MediaObjects/41586 2026 10764 MOESM1 ESM.pdf Supplementary discussion, methods and results Sections 1-16 . Contains related work, details on the system design for the Mx agent and Dialogue agent, details on the OSCE evaluation study inter-rater reliability analysis, clinician metadata, scenario metadata, ablation analysis , and methods details and further results for the RxQA medication reasoning benchmark. Supplementary Data 1 download PDF https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-026-10764-5/MediaObjects/41586 2026 10764 MOESM3 ESM.pdf Detailed view of two sample scenarios with AMIE and PCP output and evaluation gradings. Full details for two sample scenarios used in the OSCE evaluation study, including scenario information, AMIE-patient-actor conversations, PCP-patient-actor conversations, specialist physician gradings and patient actor gradings for all three visits per scenario. Supplementary Data 2 download PDF https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-026-10764-5/MediaObjects/41586 2026 10764 MOESM4 ESM.pdf Details for all 120 OSCE scenarios with AMIE output PDF . Scenario details and AMIE output for all 120 scenarios used either in the OSCE evaluation study 100 or for validation purposes 20 , in human-readable PDF format. Supplementary Data 3 download CSV https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-026-10764-5/MediaObjects/41586 2026 10764 MOESM5 ESM.csv Details for all 120 OSCE scenarios with AMIE output CSV . Scenario details and AMIE output for all 120 scenarios used either in the OSCE evaluation study 100 or for validation purposes 20 , in machine-readable CSV format. Rights and permissions About this article Cite this article Liévin, V., Palepu, A., Weng, WH. et al. Towards Conversational AI for Disease Management. Nature 2026 . https://doi.org/10.1038/s41586-026-10764-5 Received: Accepted: Published: DOI: https://doi.org/10.1038/s41586-026-10764-5