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 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]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293
Flagged first, then by citations
Research works printed but not linked: 9
- Wu, 2023, Autogen: Enabling next-gen llm applications via multi-agent conversation framework, arXiv
- LeCun, 2022, A path towards autonomous machine intelligence, Open Rev., 1, 1
- Saha, 2023, Can language models teach weaker agents? teacher explanations improve students via theory of mind, arXiv
- Zhou, 2023, Llm as dba, arXiv
- Zhu, 2023, Ghost in the minecraft: Generally capable agents for open-world enviroments via large language models with text-based knowledge and memory, arXiv
- Chen, 2023, Quantifying uncertainty in answers from any language model via intrinsic and extrinsic confidence assessment, arXiv
- Shafer, 2008, A tutorial on conformal prediction, J. Mach. Learn. Res., 9, 371
- Huang, 2023, Benchmarking large language models as ai research agents, arXiv
- 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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Sorted by citations
Items 301–322 of 322
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Wang, J., Liu, C., Zhan, X., Wu, Z., Peng, R., & Zhang, X. (2026) · Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · doi:10.1177/09544100261489928
Albashrawi, M. A. (2026) · Information Discovery and Delivery · doi:10.1108/idd-09-2025-0235
López-López, D., Marco-Simó, J. M., Martínez, R. G., Cruz-Suárez, A., & Torres-Pruñonosa, J. (2026) · Applied Artificial Intelligence · doi:10.1080/08839514.2026.2684087
PathLit-Agent: A Multi-stage AI Literature Mining System for Pathway-Focused Biomedical Insight
Maghsoudi, Z., & Harris, F. C. (2026) · Advances in intelligent systems and computing · doi:10.1007/978-3-032-27214-0_35
de Araujo, H. H. D. V., & Scalabrin, E. E. (2026) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa71656.2026.11705594
Process-aware inference of biomedical Raman spectra classification
Kourkoumelis, N. (2026) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2026.105914
Shifting from knowledge retrieval to evidence exploration and synthesis
Shikhare, S., Cohen-Setton, J., & Bulusu, K. C. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01313-w
Toward adaptive cyborg tissues.
Li, Q., Lunshof, J., & Liu, J. (2026) · Trends in Biotechnology · doi:10.1016/j.tibtech.2026.09.009
Sasono, M. A. H., Putra, A. N., & Anam, K. (2026) · Asian Control Conference (ASCC) · doi:10.1109/ascc70886.2026.11701851
Zhou, Y., Li, H., Feng, Z., Chen, Y., Tang, S., Srivastava, S., Kumar, A., Guan, L., Pan, Y., Wang, J., et al. (2026) · Dyes and Pigments · doi:10.1016/j.dyepig.2026.114259
The rise of agentic AI in drug discovery from static tools to autonomous virtual scientists
Zhao, D., Fang, M., Mao, L., Zhu, Y., Su, Q., Shi, H., Xu, H., Li, L., Hou, T., & Hsieh, C.-Y. (2026) · npj Drug Discovery. · doi:10.1038/s44386-026-00075-0
Bressan, M., Meijer, E. N., Hinić, V., Ellendorff, T., Goldman, J.-P., Kew, T., Nolte, O., Schneider, G., Egli, A., Ahlström, M., et al. (2026) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2026.09.035
LLM4PDA: Augmenting piRNA-Disease Association Prediction with Large Language Models
Yao, D., Li, X., Zhan, X., Zhang, B., & Zhang, J. (2026) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-026-00874-z
Distributed AI agents enable comprehensive discovery of biological insights at scalePreprint
Sun, A., Park, J. J., DeTomaso, D., Kim, S., Liu, A., Shringarpure, S., Kim, K., Thompson, P. C., Shi, T., Wang, Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.15.759436
PRAXIS-MD: a long-term brain-enhanced agent framework for end-to-end molecular dynamics research
Ma, Z., Yang, C., Zhu, J., Song, Y., Yang, L., Xu, L., Xiao, M., & Jiang, X. (2026) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2026.109623
Multi-organ imaging genetics: towards an integrated view of brain, body and brain diseases
Fan, Z., & Zhao, B. (2026) · Nature Reviews Neurology · doi:10.1038/s41582-026-01276-x
Martínez-de-Morentin, X., Khan, S., Cenzano, I., Lehmann, R., Maillo, A., Lagani, V., Tegnér, J., & Gómez-Cabrero, D. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01311-y
Zhang, G., Luo, Y., Ju, F., Liu, D., Zhang, D., Changliang, 郭. G., Han, J., Qiu, S., Wang, T., Wang, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.18.759043
AI-driven analysis of genomic alterations in colorectal cancer patients
Velazquez-Villarreal, E. I. (2026) · Future Oncology · doi:10.1080/14796694.2026.2751396
Agentic AI systems for scientific discovery
Feng, B., Walther, P., Zhang, L., Coyle, R. A., Kim, S., Mandal, A., Zhang, Z., Papidocha, S. M., Kelmanson, A., Segura, D. M., et al. (2026) · Nature Reviews Methods Primers · doi:10.1038/s43586-026-00534-y · Cited by 1
StemGuard : An Expertise‐Reinforced and Voice‐Controlled Agent for Stem Cell Culture Surveillance
Li, K., Guo, Y.-T., Chen, Z., & Niu, Y.-B. (2026) · Expert systems · doi:10.1111/exsy.70452
An immune world model for multiscale forecasting and therapeutic hypothesis generation
Cui, T., Wang, X., Li, Z., Ding, J., You, L., Xu, Y., Xu, W., Wu, F., Ying, K., Ouyang, W., et al. (2026) · arXiv · doi:10.48550/arxiv.2609.14709
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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