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Cited by 322Artificial Intelligence in Healthcare and EducationArtificial Intelligence ApplicationsScientific Computing and Data Management

The DOI matches its Crossref record; 207 of the 207 references it cites were found in a registry or an index.

Empowering biomedical discovery with AI agents

Open access
Gao, Shanghua, Fang, Ada, Huang, Yepeng, Giunchiglia, Valentina, Noori, Ayush, Schwarz, Jonathan Richard, Ektefaie, Yasha, Kondic, Jovana, Zitnik, Marinka·2024·Cell
DOI 10.1016/j.cell.2024.09.022
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Abstract

We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.

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Show all 199 references

Research works printed but not linked: 9

  1. Wu, 2023, Autogen: Enabling next-gen llm applications via multi-agent conversation framework, arXiv
  2. LeCun, 2022, A path towards autonomous machine intelligence, Open Rev., 1, 1
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  5. Zhu, 2023, Ghost in the minecraft: Generally capable agents for open-world enviroments via large language models with text-based knowledge and memory, arXiv
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  9. Guha, 2023, Ai regulation has its own alignment problem: The technical and institutional feasibility of disclosure, registration, licensing, and auditing, George Wash. Law Rev., 11

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Metadata

Authors
Gao, S., Fang, A., Huang, Y., Giunchiglia, V., Noori, A., Schwarz, J. R., Ektefaie, Y., Kondic, J., & Zitnik, M.
Affiliations
  • Harvard University (US)
  • Massachusetts Institute of Technology (US)
ORCID
Venue
Cell
Publisher
Elsevier BV
Published
2024-10
Volume
187
Issue
22
Pages
6125-6151
ISSN
0092-8674 (ISSN-L)
DOI
10.1016/j.cell.2024.09.022
OpenAlex
W4403925918
Cited by
315
References
199
Citations by year
2026: 168 · 2025: 135 · 2024: 12
FWCI
7.19
Type
journal article
Language
en
Open access
Licence
Locations
  • Cell (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
  • Medical Research Council (T32 HG002295, MR/W00710X/1)
  • National Institutes of Health (R01-HD108794)
  • Harvard Medical School
  • National Science Foundation (2339524, FA8702-15-D-0001)
  • Aligning Science Across Parkinson's
  • Harvard Data Science Initiative, Harvard University
  • The Chan Zuckerberg Initiative
  • National Human Genome Research Institute
  • Milken Institute
  • Google
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
Keywords
AI agents, AI scientists, biomedical discovery, large language models, generative models, continual learning, structured memory, hypothesis generation, therapy design, human-AI collaboration
MeSH terms
Machine Learning, Artificial Intelligence, Humans, Biomedical Research

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