AI succeeds in diagnosing rare diseases

Nature News · Feb 18, 2026 · Collected from RSS

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NEWS AND VIEWS 18 February 2026 An artificial-intelligence system uses clinical data, genetic information and literature searches to suggest diagnoses and provides the underlying reasoning. By Timo Lassmann Timo Lassmann is at The Kids Research Institute Australia and the UWA Centre for Child Health Research, The University of Western Australia, Nedlands, Western Australia 6009, Australia. For the estimated 300 million people1 living with a rare disease worldwide, obtaining an accurate diagnosis remains an arduous journey. The ‘diagnostic odyssey’ often takes five years or more2, and is marked by repeated specialist consultations, misdiagnoses and unnecessary treatments. With more than 7,000 rare diseases recognized3, most of which affect fewer than one in 2,000 individuals, even experienced clinicians struggle to connect unfamiliar patterns of symptoms with their underlying causes. Writing in Nature, Zhao et al.4 present an artificial-intelligence system called DeepRare, which uses a range of specialized tools and knowledge sources to generate ranked diagnostic hypotheses for rare diseases, each accompanied by reasoning that links the conclusions to verifiable medical evidence. Access options Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription $32.99 / 30 days cancel any time Subscribe to this journal Receive 51 print issues and online access $199.00 per year only $3.90 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 Additional access options: Log in Learn about institutional subscriptions Read our FAQs Contact customer support doi: https://doi.org/10.1038/d41586-026-00290-9T.L. used Anthropic’s Claude Opus 4.5 to help prepare this News & Views article. ReferencesNguengang Wakap, S. et al. Eur. J. Hum. Genet. 28, 165–173 (2020).Article PubMed Google Scholar Faye, F. et al. Eur. J. Hum. Genet. 32, 1116–1126 (2024).Article PubMed Google Scholar Haendel, M. et al. Nature Rev. Drug Discovery 19, 77–78 (2019).Article Google Scholar Zhao, W. et al. Nature https://doi.org/10.1038/s41586-025-10097-9 (2026).Article Google Scholar Dawkins, H. J. S. et al. Clin. Transl Sci. 11, 11–20 (2018).Article PubMed Google Scholar Groza, T. et al. Hum. Genet. Genom. Adv. 7, 100558 (2026).Article Google Scholar Boiko, D. A., MacKnight, R., Kline, B. & Gomes, G. Nature 624, 570–578 (2023).Article PubMed Google Scholar Zou, J. & Topol, E. J. Lancet 405, 457 (2025).Article Google Scholar Download references Competing Interests The author declares no competing interests. Related Articles Read the paper: An agentic system for rare disease diagnosis with traceable reasoning See all News & Views Subjects Latest on: Medical research Machine learning Genomics AI offers way to image and assess clinical cell samples News & Views 18 FEB 26 Gene editing treats a mouse model of a neurodevelopmental disorder News & Views 18 FEB 26 CAR-T therapy provides relief for children with autoimmune diseases Research Highlight 13 FEB 26 AI offers way to image and assess clinical cell samples News & Views 18 FEB 26 Will self-driving ‘robot labs’ replace biologists? Paper sparks debate News 18 FEB 26 Why sky-high pay for AI researchers is bad for the future of science Comment 17 FEB 26 Ancient co-option of LTR retrotransposons as yeast centromeres Article 18 FEB 26 Malaria is hindered by repression of a cell-cycle protein News & Views 18 FEB 26 Hunter-gatherers took refuge in European ‘water world’ for millennia News 11 FEB 26