Evidence map›Paper›PMID 40501924›Full record

ArticlebioRxiv : the preprint server for biology2025

Biomni: A General-Purpose Biomedical AI Agent.

Kexin Huang, Serena Zhang, Hanchen Wang, Yuanhao Qu, Yingzhou Lu, Yusuf Roohani, Ryan Li, Lin Qiu, Gavin Li, Junze Zhang and 13 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. ADAPT: a programme for the advanced detection of AI-enabled pathogenic threats.Frontiers in bioengineering and biotechnology · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Kexin HuangDepartment of Computer Science, Stanford University School of Engineering, Stanford, CA, USA.
Serena ZhangDepartment of Computer Science, Stanford University School of Engineering, Stanford, CA, USA.
Hanchen WangDepartment of Computer Science, Stanford University School of Engineering, Stanford, CA, USA.
Yuanhao QuDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Yingzhou LuDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Yusuf RoohaniDepartment of Computer Science, Stanford University School of Engineering, Stanford, CA, USA.
Ryan LiDepartment of Computer Science, Stanford University School of Engineering, Stanford, CA, USA.
Lin QiuPaul G. Allen School of Computer Science and Engineering, University ofWashington,WA, USA.
Gavin LiDepartment of Computer Science, Stanford University School of Engineering, Stanford, CA, USA.
Junze ZhangDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Di YinDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Shruti MarwahaDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Jennefer N CarterDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Xin ZhouDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Matthew WheelerDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Jonathan A BernsteinDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Mengdi WangDepartment of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA.
Peng HeDepartment of Pathology, University of California San Francisco, San Francisco, CA, USA.
Jingtian ZhouArc Institute, Palo Alto, CA, USA.
Michael SnyderDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Le CongDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Aviv RegevResearch and Early Development, Genentech, South San Francisco, CA, USA.
Jure LeskovecDepartment of Computer Science, Stanford University School of Engineering, Stanford, CA, USA.

Funding

Center for Undiagnosed Diseases at StanfordU01NS134358 · NINDS · STANFORD UNIVERSITY · PI Jonathan Adam Bernstein, HOLLY K TABOR · 2023 to 2026
$3.1M
NINDS NIH HHS U01 NS134358
6 · The paper itself

Abstract

Biomedical research underpins progress in our understanding of human health and disease, drug discovery, and clinical care. However, with the growth of complex lab experiments, large datasets, many analytical tools, and expansive literature, biomedical research is increasingly constrained by repetitive and fragmented workflows that slow discovery and limit innovation, underscoring the need for a fundamentally new way to scale scientific expertise. Here, we introduce Biomni, a general-purpose biomedical AI agent designed to autonomously execute a wide spectrum of research tasks across diverse biomedical subfields. To systematically map the biomedical action space, Biomni first employs an action discovery agent to create the first unified agentic environment - mining essential tools, databases, and protocols from tens of thousands of publications across 25 biomedical domains. Built on this foundation, Biomni features a generalist agentic architecture that integrates large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, enabling it to dynamically compose and carry out complex biomedical workflows - entirely without relying on predefined templates or rigid task flows. Systematic benchmarking demonstrates that Biomni achieves strong generalization across heterogeneous biomedical tasks - including causal gene prioritization, drug repurposing, rare disease diagnosis, microbiome analysis, and molecular cloning - without any task-specific prompt tuning. Real-world case studies further showcase Biomni's ability to interpret complex, multi-modal biomedical datasets and autonomously generate experimentally testable protocols. Biomni envisions a future where virtual AI biologists operate alongside and augment human scientists to dramatically enhance research productivity, clinical insight, and healthcare. Biomni is ready to use at https://biomni.stanford.edu, and we invite scientists to explore its capabilities, stress-test its limits, and co-create the next era of biomedical discoveries.

Identifiers

PMID40501924
PMCPMC12157518

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.