medRxiv : the preprint server for health sciences · 2026
OpenScientist: evaluating an open agentic AI co-scientist to accelerate biomedical discovery.
СтатьяПрепринтНазвание и аннотация: Перевод в очереди
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Roberts Kaleigh F, Abrams Zachary B, Cappelletti Luca, Moqri Mahdi, Heugel Nicholas, Caufield J Harry, Bourdenx Mathieu, Li Yan, Banerjee Jineta, Foschini Luca, Galeano Diego, Harris Nomi L, Li Melody, Ying Kejun, Melendez Justin A, Barthélemy Nicolas R, Bollinger James G, He Yingxin, Ovod Vitaliy, Benzinger Tammie L S, Flores Shaney, Gordon Brian A, Ojewole Adegoke A, Phatak Mukta, Elbert Donald L, Biber Sarah, Landsness Eric C, Mungall Christopher J, Bateman Randall J, Reese Justin T
Аннотация
BACKGROUND: Advances in medicine depend on analyzing large and complex data sources, but discovery is partly constrained by the limited time and domain expertise of human researchers. Agentic artificial intelligence (agentic AI) can accelerate discovery by automating components of the scientific workflow, including information retrieval, data analysis, and knowledge synthesis.
AIM: OpenScientist, an open-source agentic AI co-scientist, aims to accelerate biomedical discovery by semi-autonomously investigating scientist-defined queries and generating clinically relevant, verifiable scientific insights.
METHODS: Domain experts evaluated OpenScientist for novel discoveries in four clinical case studies: (1) a prespecified analysis in a community-based Alzheimer's disease biomarker cohort, (2) unsupervised modeling for plasma proteomic survival prediction, (3) hypothesis investigation in single-cell transcriptomic data from neurons with neurofibrillary tangles, and (4) hypothesis generation with validation in a multiple myeloma dataset with a randomized negative control.
RESULTS: OpenScientist completed analyses in minutes that otherwise would take weeks to months of human time and expertise. It identified %ptau217 as the best predictor of amyloid PET status, generated a plasma proteomic survival model with performance comparable to published models, proposed a mechanism linking tau pathology to altered lysosomal acidification, and generated multiple myeloma hypotheses that were validated in an external cohort while distinguishing true signal from randomized controls.
CONCLUSION: OpenScientist demonstrates that open, auditable, agentic AI can support real-world clinical research by generating hypotheses, executing analyses, and discovering insights from complex datasets.
AIM: OpenScientist, an open-source agentic AI co-scientist, aims to accelerate biomedical discovery by semi-autonomously investigating scientist-defined queries and generating clinically relevant, verifiable scientific insights.
METHODS: Domain experts evaluated OpenScientist for novel discoveries in four clinical case studies: (1) a prespecified analysis in a community-based Alzheimer's disease biomarker cohort, (2) unsupervised modeling for plasma proteomic survival prediction, (3) hypothesis investigation in single-cell transcriptomic data from neurons with neurofibrillary tangles, and (4) hypothesis generation with validation in a multiple myeloma dataset with a randomized negative control.
RESULTS: OpenScientist completed analyses in minutes that otherwise would take weeks to months of human time and expertise. It identified %ptau217 as the best predictor of amyloid PET status, generated a plasma proteomic survival model with performance comparable to published models, proposed a mechanism linking tau pathology to altered lysosomal acidification, and generated multiple myeloma hypotheses that were validated in an external cohort while distinguishing true signal from randomized controls.
CONCLUSION: OpenScientist demonstrates that open, auditable, agentic AI can support real-world clinical research by generating hypotheses, executing analyses, and discovering insights from complex datasets.