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Empowering biomedical discovery with AI agents
Open accessAbstract
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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“collaborative agent teams have produced superior results on complex bioinformatics analyses and clinical decision support challenges where no single model excels [1,4].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [4]
In the citing paper
“In various domains, genomics, electronic health records, imaging, and beyond, MAS approaches have already demonstrated improved performance, reliability, and explainability compared to single-agent methods.”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [4]
In the citing paper
“Furthermore, another critical feature of LLMs is their capability to use tools for automated online search, task design, and experiment execution [203–205], which exhibits their further capabilities in automatically collecting and preprocessing biomedical datasets, designing bioinformatic analyses to identify disease-related molecular regulations, conducting literature reviews for result inte”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [205]
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Coquart, P., Haddad, A. E., Koutsouras, D. A., & Bolander, J. (2025) · Biosensors · doi:10.3390/bios15040253 · Cited by 11
Epistemic responsibility: toward a community standard for human-AI collaborations
Lloyd, D. E. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1635691 · Cited by 12
Role of Natural Polymers in Hydrogel-Based Scaffolds for Tissue Engineering Applications
Prakashini, R. S., Thangam, T., Hemamalani, A. U., & Parthasarathy, K. (2025) · Regenerative Engineering and Translational Medicine · doi:10.1007/s40883-025-00462-5 · Cited by 8
Agentic Artificial Intelligence: Legal and Ethical Challenges of Autonomous Systems
Bowen, G. L. (2025) · Journal of Digital Technologies and Law · doi:10.21202/jdtl.2025.17 · Cited by 8
Operating advanced scientific instruments with AI agents that learn on the job
Vriza, A., Prince, M. H., Zhou, T., Chan, H., & Cherukara, M. J. (2026) · npj Computational Materials · doi:10.1038/s41524-026-02005-0 · Cited by 8
AI-driven discovery of GPNMB CAR T cells as a multi-cancer therapy
Baker, D. J., Frommer, L. M., Uslu, U., Patel, K. K., Zhu, D., Engel, N. W., George, J. M., Zhao, W., Kim, S. I., Sun, L., et al. (2026) · Cell · doi:10.1016/j.cell.2026.06.002 · Cited by 9
Spike sorting AI agentPreprint
Lin, Z., Marin‐Llobet, A., Baek, J., He, Y., Lee, J., Wang, W., Zhang, X., Lee, A. J., Liang, N., Du, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.11.637754 · Cited by 16
Prasad, D., Khandeshi, A., Sartin, S., Jain, R., Dahdaleh, N. S., Lesniak, M. S., Luo, Y., & Ahuja, C. S. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01859-w · Cited by 9
From visual question answering to intelligent AI agents in ophthalmology
Chen, X., Chen, R., Xu, P., Wan, X., Zhang, W., Yan, B., Shang, X., He, M., & Shi, D. (2025) · British Journal of Ophthalmology · doi:10.1136/bjo-2024-326097 · Cited by 9
BioContextAI is a community hub for agentic biomedical systems
Kuehl, M. B., Schaub, D. P., Carli, F., Heumos, L., Hellmig, M., Fernández‐Zapata, C., Kaiser, N., Schaul, J., Kulaga, A. Y., Usanov, N., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02900-9 · Cited by 11
Chat Demeter: a multi-agent system for plant disease diagnosis integrating CNN-transformer models
Zhang, S. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1695227 · Cited by 7
Choi, J. Y., & Yoo, T. K. (2026) · Discover Health Systems · doi:10.1007/s44250-026-00343-w · Cited by 10
Medea: An AI agent for therapeutic reasoning across biological contextsPreprint
Sui, P., Li, M. M., Munson, B. P., Gao, S., Shen, W., Giunchiglia, V., Shen, A. N., Huang, Y., Kong, Z., Licon, K., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.16.696667 · Cited by 13
Self-citationSuenghataiphorn, T., Danpanichkul, P., Tribuddharat, N., & Kulthamrongsri, N. (2025) · Journal of Clinical and Experimental Hepatology · doi:10.1016/j.jceh.2025.102627 · Cited by 6
Bridging BioSciences and technology: The impact of AI & GenAI in life sciences and agribusiness
da Cunha, N. B., Fernandes, F. C., Gil-Ley, A., Franco, O. L., Timakondu, N., & Costa, F. F. (2025) · Gene · doi:10.1016/j.gene.2025.149623 · Cited by 7
Lessons from the Failure of Canada’s Artificial Intelligence and Data Act
Ishaque, A. H., Aidid, A., & Zadeh, G. (2025) · NEJM AI · doi:10.1056/aipc2500153 · Cited by 5
Decoding Immunodeficiencies with Artificial Intelligence: A New Era of Precision Medicine
Sciaccotta, R., Barone, P., Murdaca, G., Fazio, M., Stagno, F., Gangemi, S., Genovese, S., & Allegra, A. (2025) · Biomedicines · doi:10.3390/biomedicines13081836 · Cited by 4
Adege, A. B. (2025) · International Journal of Clinical Practice · doi:10.1155/ijcp/9888902 · Cited by 6
Biomedical AI: Evolving from digital to physical and biological intelligence
Li, Z., Qin, W., Liang, D., & Zheng, H. (2025) · The Innovation Informatics · doi:10.59717/j.xinn-inform.2025.100010 · Cited by 7
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)
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2024-10
- Volume
- 187
- Issue
- 22
- Pages
- 6125-6151
- ISSN
- 0092-8674 (ISSN-L)
- 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
- hybrid
- doi.org
- 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
- 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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