Federated benchmarking of medical artificial intelligence with MedPerf
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Published:2023-07-17
Issue:7
Volume:5
Page:799-810
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ISSN:2522-5839
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Container-title:Nature Machine Intelligence
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language:en
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Short-container-title:Nat Mach Intell
Author:
Karargyris AlexandrosORCID, Umeton RenatoORCID, Sheller Micah J.ORCID, Aristizabal Alejandro, George Johnu, Wuest Anna, Pati SarthakORCID, Kassem HasanORCID, Zenk MaximilianORCID, Baid Ujjwal, Narayana Moorthy Prakash, Chowdhury Alexander, Guo Junyi, Nalawade SahilORCID, Rosenthal JacobORCID, Kanter David, Xenochristou Maria, Beutel Daniel J., Chung Verena, Bergquist TimothyORCID, Eddy James, Abid Abubakar, Tunstall Lewis, Sanseviero Omar, Dimitriadis Dimitrios, Qian YimingORCID, Xu XinxingORCID, Liu Yong, Goh Rick Siow Mong, Bala Srini, Bittorf Victor, Puchala Sreekar Reddy, Ricciuti Biagio, Samineni SoujanyaORCID, Sengupta Eshna, Chaudhari AkshayORCID, Coleman Cody, Desinghu Bala, Diamos Gregory, Dutta Debo, Feddema Diane, Fursin GrigoriORCID, Huang Xinyuan, Kashyap SatyanandaORCID, Lane Nicholas, Mallick Indranil, Mascagni PietroORCID, Mehta VirendraORCID, Moraes Cassiano Ferro, Natarajan Vivek, Nikolov Nikola, Padoy Nicolas, Pekhimenko Gennady, Reddi Vijay Janapa, Reina G. Anthony, Ribalta Pablo, Singh Abhishek, Thiagarajan Jayaraman J., Albrecht Jacob, Wolf Thomas, Miller Geralyn, Fu HuazhuORCID, Shah Prashant, Xu Daguang, Yadav Poonam, Talby David, Awad Mark M., Howard Jeremy P., Rosenthal Michael, Marchionni LuigiORCID, Loda MassimoORCID, Johnson Jason M.ORCID, Bakas SpyridonORCID, Mattson PeterORCID, , ,
Abstract
AbstractMedical 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.
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software
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