{"slug": "towards-conversational-ai-for-disease-management", "title": "Towards Conversational AI for Disease Management", "summary": "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.", "body_md": "## Abstract\n\nWhile 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.\n\nThis 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)\n\n## Access options\n\nAccess Nature and 54 other Nature Portfolio journals\n\nGet Nature+, our best-value online-access subscription\n\n27,99 € / 30 days\n\ncancel any time\n\nSubscribe to this journal\n\nReceive 52 print issues and online access\n\n199,00 € per year\n\nonly 3,83 € per issue\n\nRent or buy this article\n\nPrices vary by article type\n\nfrom$1.95\n\nto$39.95\n\nPrices may be subject to local taxes which are calculated during checkout\n\n## Author information\n\n### Authors and Affiliations\n\n### Corresponding authors\n\n## Supplementary information\n\n[Supplementary Information (download PDF )](https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-026-10764-5/MediaObjects/41586_2026_10764_MOESM1_ESM.pdf)\n\nSupplementary 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.\n\n[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)\n\nDetailed 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.\n\n[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)\n\nDetails 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.\n\n[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)\n\nDetails 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.\n\n## Rights and permissions\n\n## About this article\n\n### Cite this article\n\nLiévin, V., Palepu, A., Weng, WH. *et al.* Towards Conversational AI for Disease Management.\n*Nature* (2026). https://doi.org/10.1038/s41586-026-10764-5\n\nReceived:\n\nAccepted:\n\nPublished:\n\nDOI: https://doi.org/10.1038/s41586-026-10764-5", "url": "https://wpnews.pro/news/towards-conversational-ai-for-disease-management", "canonical_source": "https://www.nature.com/articles/s41586-026-10764-5", "published_at": "2026-06-17 15:08:42+00:00", "updated_at": "2026-06-17 15:22:55.405069+00:00", "lang": "en", "topics": ["large-language-models", "artificial-intelligence", "ai-research", "ai-products", "ai-safety"], "entities": ["Google", "AMIE", "Gemini", "NICE", "BMJ", "RxQA"], "alternates": {"html": "https://wpnews.pro/news/towards-conversational-ai-for-disease-management", "markdown": "https://wpnews.pro/news/towards-conversational-ai-for-disease-management.md", "text": "https://wpnews.pro/news/towards-conversational-ai-for-disease-management.txt", "jsonld": "https://wpnews.pro/news/towards-conversational-ai-for-disease-management.jsonld"}}