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Federated benchmarking of medical artificial intelligence with MedPerf
Open accessAbstract
Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.
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“Beyond training, AI models must also be tested across multiple independent sites and evaluated for overall accuracy and potential bias (162).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [162]
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Cites
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
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741
Self-citationk-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 2×
“…studies have shown that models trained with data from limited and specific clinical settings are often biased with respect to specific patient populations 11–13 ; such data biases can lead to models that seem promising during development but have lower performance in wider deployment 14,15 .”
The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 4,015 · cited 1×
“In a federated evaluation platform, data are always accessed and analysed locally.”
Self-citationThe EU General Data Protection Regulation (GDPR)
Voigt, P., & von dem Bussche, A. (2017) · doi:10.1007/978-3-319-57959-7 · Cited by 3,001
Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002683 · Cited by 1,719 · cited 1×
“…studies have shown that models trained with data from limited and specific clinical settings are often biased with respect to specific patient populations 11–13 ; such data biases can lead to models that seem promising during development but have lower performance in wider deployment 14,15 .”
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646
Self-citationDeep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Hitaj, B., Ateniese, G., & Perez-Cruz, F. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134012 · Cited by 1,377
Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365 · cited 1×
“• Data model standardization efforts to enable collaboration between institutions, such as the OMOP Common Data Model 80,81 , possibly leveraging the highly multimodal nature of biomedical data 82 .”
Research works printed but not linked: 3
- Bakas, S. et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BraATS challenge. Preprint at https://arxiv.org/abs/1811.02629 (2018). · arXiv
- Ul Haq, H., Kocaman, V. & Talby, D. Deeper clinical document understanding using relation extraction. In Proc. Workshop on Scientific Document Understanding (eds Veyseh, A. P. B. et al.) Vol. 3164 (CEUR-WS, 2022).
- SIG for Challenges (MICCAI, 2023); http://www.miccai.org/special-interest-groups/challenges/
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Self-citationArtificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
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Nam, Y., Kim, D., Kyung, S., Seo, J., Song, J. M., Kwon, J., Kim, J., Jo, W., Park, H. J., Sung, J., et al. (2025) · Korean Journal of Radiology · doi:10.3348/kjr.2025.0599 · Cited by 106
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Self-citationNaik, K., Goyal, R. K., Foschini, L., Chak, C. M., Thielscher, C., Zhu, H., Lu, J. J.-Q., Lehár, J., Pacanoswki, M. A., Terranova, N., et al. (2023) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.3152 · Cited by 84
Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey
Le, M., Huynh‐The, T., Do‐Duy, T., Vu, T.-H., Hwang, W., & Pham, Q. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3427324 · Cited by 94
Yuan, M., Bao, P., Yuan, J., Shen, Y., Chen, Z., Xie, Y., Zhao, J., Li, Q., Chen, Y., Zhang, L., et al. (2024) · Medicine Plus · doi:10.1016/j.medp.2024.100030 · Cited by 93
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Matta, S. C., Lamard, M., Zhang, P., Guilcher, A. L., Borderie, L., Cochener, B., & Quellec, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109256 · Cited by 83
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Shah, S. T., Ali, Z., Waqar, M., & Kim, A. (2025) · Healthcare · doi:10.3390/healthcare13212760 · Cited by 41
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Self-citationSun, C., McEwan, A. L., Boulton, K., Demetriou, E. A., Sadozai, A. K., Lampit, A., & Guastella, A. J. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105931 · Cited by 22
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“The BraTS 2023 challenge has expanded to a cluster of challenges, encompassing a variety of tumor types alongside augmentation tasks [1,10,15,17,19,25].”
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Self-citationvan Breugel, M., Greenhawt, M., Eguíluz‐Gracia, I., Torres, M. J., Anagnostou, A., & Koppelman, G. H. (2025) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2025.08.022 · Cited by 15
Rowaiye, A. B., Folarin, A. A., Akingbade, T. O., Okoli, J. C., Rowaiye, O. I., Folorunso, T. R., & Bur, D. (2025) · Discover Chemistry. · doi:10.1007/s44371-025-00363-0 · Cited by 16
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Gonzalez, J. A. H., & Colom, M. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1491823 · Cited by 20
Yamaoka, T., & Watanabe, S. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110855 · Cited by 15
Kazerooni, A. F., Familiar, A., Aboian, M., Brüningk, S. C., Vossough, A., Linguraru, M. G., Huang, R. Y., Hargrave, D. R., Peet, A. C., Resnick, A. C., et al. (2025) · The Lancet Oncology · doi:10.1016/s1470-2045(25)00489-9 · Cited by 14
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Welten, S., Herr, M. D. A. B., Hempel, L., Hieber, D., Placzek, P., Graf, M., Weber, S., Neumann, L., Jugl, M., Tirpitz, L., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03450-6 · Cited by 12
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Wu, J., Gu, B., Zhou, R., Xie, K., Snyder, D., Jiang, Y., Carducci, V., Wyss, R., Desai, R. J., Alsentzer, E., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01719-2 · Cited by 28
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Oyekan, B. (2024) · Journal of Knowledge Learning and Science Technology · doi:10.60087/jklst.vol3.n3.p139-164 · Cited by 6
Bajunaid, R., Niu, C., Hambly, C., Liu, Z., Yamada, Y., Alemán-Mateo, H., Anderson, L. J., Arab, L., Baddou, I., Bandini, L. G., et al. (2025) · Nature Food · doi:10.1038/s43016-025-01175-2 · Cited by 7
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Yoo, W. (2026) · BJC Reports · doi:10.1038/s44276-026-00221-1 · Cited by 6
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Lennerz, J. K. (2024) · Die Pathologie · doi:10.1007/s00292-024-01396-5 · Cited by 5
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Shodiev, D., Ushakov, E. N., Litvinov, A., & Markin, Y. V. (2025) · Informatics · doi:10.3390/informatics12040112 · Cited by 5
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Self-citation
Metadata
- Authors
- Karargyris, A., Umeton, R., Sheller, M. J., Aristizabal, A., George, J., Wuest, A., Pati, S., Kassem, H., Zenk, M., Baid, U., Narayana Moorthy, P., Chowdhury, A., Guo, J., Nalawade, S., Rosenthal, J., Kanter, D., Xenochristou, M., Beutel, D. J., Chung, V., Bergquist, T., Eddy, J., Abid, A., Tunstall, L., Sanseviero, O., Dimitriadis, D., Qian, Y., Xu, X., Liu, Y., Goh, R. S. M., Bala, S., Bittorf, V., Puchala, S. R., Ricciuti, B., Samineni, S., Sengupta, E., Chaudhari, A., Coleman, C., Desinghu, B., Diamos, G., Dutta, D., Feddema, D., Fursin, G., Huang, X., Kashyap, S., Lane, N., Mallick, I., FeTS Consortium, BraTS-2020 Consortium, AI4SafeChole Consortium, Mascagni, P., Mehta, V., Moraes, C. F., Natarajan, V., Nikolov, N., Padoy, N., Pekhimenko, G., Reddi, V. J., Reina, G. A., Ribalta, P., Singh, A., Thiagarajan, J. J., Albrecht, J., Wolf, T., Miller, G., Fu, H., Shah, P., Xu, D., Yadav, P., Talby, D., Awad, M. M., Howard, J. P., Rosenthal, M., Marchionni, L., Loda, M., Johnson, J. M., Bakas, S., & Mattson, P.
- Affiliations
- Institut de Chirurgie Guidée par l'Image (FR)
- Harvard University (US)
- Intel (United States) (US)
- Nutanix (United States) (US)
- University of Pennsylvania (US)
- Université de Strasbourg (FR)
- German Cancer Research Center (DE)
- Dana-Farber Cancer Institute (US)
- Stanford University (US)
- University of Cambridge (GB)
- Sage Bionetworks (US)
- Hugging Face (US)
- Microsoft (United States) (US)
- Agency for Science, Technology and Research (SG)
- Supermicro (United States) (US)
- Meta (United States) (US)
- Stanford Medicine (US)
- Rutgers, The State University of New Jersey (US)
- Red Hat (United States) (US)
- Cisco Systems (United States) (US)
- IBM (United States) (US)
- Tata Medical Center (IN)
- Agostino Gemelli University Polyclinic (IT)
- University of Trento (IT)
- Google (United States) (US)
- University of Toronto (CA)
- Nvidia (United States) (US)
- Massachusetts Institute of Technology (US)
- Lawrence Livermore National Laboratory (US)
- University of York (GB)
- John Snow (United States) (US)
- The University of Queensland (AU)
- Brigham and Women's Hospital (US)
- Weill Cornell Medicine (US)
- Broad Institute (US)
- Corresponding author
- Karargyris, Alexandros
- Umeton, Renato
- Sheller, Micah J.
- Johnson, Jason M.
- Bakas, Spyridon
- Mattson, Peter
- ORCID
- Karargyris, Alexandros
- Umeton, Renato
- Sheller, Micah J.
- Pati, Sarthak
- Kassem, Hasan
- Zenk, Maximilian
- Baid, Ujjwal
- Guo, Junyi
- Nalawade, Sahil
- Rosenthal, Jacob
- Xenochristou, Maria
- Beutel, Daniel J.
- Chung, Verena
- Bergquist, Timothy
- Eddy, James
- Abid, Abubakar
- Dimitriadis, Dimitrios
- Qian, Yiming
- Xu, Xinxing
- Liu, Yong
- Goh, Rick Siow Mong
- Ricciuti, Biagio
- Samineni, Soujanya
- Chaudhari, Akshay
- Fursin, Grigori
- Huang, Xinyuan
- Kashyap, Satyananda
- Lane, Nicholas
- Mallick, Indranil
- Mascagni, Pietro
- Mehta, Virendra
- Moraes, Cassiano Ferro
- Nikolov, Nikola
- Padoy, Nicolas
- Pekhimenko, Gennady
- Reddi, Vijay Janapa
- Reina, G. Anthony
- Singh, Abhishek
- Thiagarajan, Jayaraman J.
- Albrecht, Jacob
- Wolf, Thomas
- Fu, Huazhu
- Shah, Prashant
- Xu, Daguang
- Yadav, Poonam
- Talby, David
- Awad, Mark M.
- Rosenthal, Michael
- Marchionni, Luigi
- Loda, Massimo
- Johnson, Jason M.
- Bakas, Spyridon
- Mattson, Peter
- Venue
- Nature Machine Intelligence
- Published
- 2023-07-17
- Volume
- 5
- Issue
- 7
- Pages
- 799-810
- ISSN
- 2522-5839 (ISSN-L)
- PMC
- PMC11068064
- OpenAlex
- W4384561507
- Cited by
- 188
- References
- 51
- Citations by year
- 2026: 74 · 2025: 55 · 2024: 49 · 2023: 9
- FWCI
- 7.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Machine Intelligence (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Digital Commons-TMC (Texas Medical Center) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Research Foundation (AISG2-TC-)
- National Research Foundation Singapore (AISG2-TC-2021-003)
- Agence Nationale de la Recherche (ANR-10-IAHU-0002, ANR-10–IAHU-02, ANR-20-CHIA-0029, ANR-10)
- National Institutes of Health (16,17)
- National Cancer Institute (U24CA248265, U24CA189523, U01CA242871)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- artificial intelligence in healthcare, healthcare data privacy, model evaluation, cloud deployment, on-premise deployment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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