Federated benchmarking of medical artificial intelligence with MedPerf

Alexandros Karargyris(Institut de Chirurgie Guidée par l'Image), Renato Umeton(Harvard University), Micah Sheller(Intel (United States)), Alejandro Aristizábal, Johnu George(Nutanix (United States)), Anna Wuest(Harvard University), Sarthak Pati(University of Pennsylvania), Hasan Kassem(Université de Strasbourg), Maximilian Zenk(German Cancer Research Center), Ujjwal Baid(University of Pennsylvania), Prakash Narayana Moorthy(Intel (United States)), Alexander Chowdhury(Dana-Farber Cancer Institute), Junyi Guo(Harvard University), Sahil Nalawade(Dana-Farber Cancer Institute), Jacob Rosenthal(Cornell University), David Kanter, Maria Xenochristou(Stanford University), Daniel J. Beutel(University of Cambridge), Verena Chung(Sage Bionetworks), Timothy Bergquist(Sage Bionetworks), James A. Eddy(Sage Bionetworks), Abubakar Abid(FACE Foundation), Lewis Tunstall(FACE Foundation), Omar Sanseviero(FACE Foundation), Dimitrios Dimitriadis(Microsoft (United States)), Yiming Qian(A*STAR Graduate Academy), Xinxing Xu(A*STAR Graduate Academy), Yong Liu(A*STAR Graduate Academy), Rick Siow Mong Goh(A*STAR Graduate Academy), Srini Bala(Supermicro (United States)), Victor Bittorf(Menlo School), Sreekar Reddy Puchala(Dana-Farber Cancer Institute), Biagio Ricciuti(Dana-Farber Cancer Institute), Soujanya Samineni(Dana-Farber Cancer Institute), Eshna Sengupta(Dana-Farber Cancer Institute), Akshay Chaudhari(Stanford University), Cody Coleman(Stanford University), Bala Desinghu(Rutgers, The State University of New Jersey), Gregory Diamos, Debo Dutta(Nutanix (United States)), Diane Feddema(Red Hat (United States)), Grigori Fursin(Seattle Institute of Oriental Medicine), Xinyuan Huang(Cisco College), Satyananda Kashyap(IBM (United States)), Nicholas D. Lane(University of Cambridge), Indranil Mallick(Tata Medical Center), Pietro Mascagni(Agostino Gemelli University Polyclinic), Virendra Mehta(University of Trento), Cassiano Ferro Moraes, Vivek Natarajan(Google (United States)), Nikola S. Nikolov(Supermicro (United States)), Nicolas Padoy(Institut de Chirurgie Guidée par l'Image), Gennady Pekhimenko(University of Toronto), Vijay Janapa Reddi(Harvard University Press), G. Anthony Reina(Intel (United States)), Pablo Ribalta(Nvidia (United States)), Abhishek Singh(Massachusetts Institute of Technology), Jayaraman J. Thiagarajan(Lawrence Livermore National Laboratory), Jacob Albrecht(Sage Bionetworks), Thomas Wolf(FACE Foundation), Geralyn M. Miller(Microsoft (United States)), Huazhu Fu(A*STAR Graduate Academy), Prashant Shah(Intel (United States)), Daguang Xu(Nvidia (United States)), Poonam Yadav(University of York), David Talby(John Snow (United States)), Mark M. Awad(Harvard University), Jeremy P. Howard(The University of Queensland), Michael H. Rosenthal(Brigham and Women's Hospital), Luigi Marchionni(Cornell University), Massimo Loda(Broad Institute), Jason M. Johnson(Dana-Farber Cancer Institute), Spyridon Bakas(RELX Group (Netherlands)), Peter Mattson(Google (United States))
Nature Machine Intelligence
July 17, 2023
Cited by 142Open Access
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Abstract

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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