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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AI models must be tested across multiple independent sites and evaluated for overall accuracy and potential bias.
“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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Self-citation
- 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 · 2022: 1
- 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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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