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MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation

Authors :
Karargyris, Alexandros
Umeton, Renato
Sheller, Micah J.
Aristizabal, Alejandro
George, Johnu
Bala, Srini
Beutel, Daniel J.
Bittorf, Victor
Chaudhari, Akshay
Chowdhury, Alexander
Coleman, Cody
Desinghu, Bala
Diamos, Gregory
Dutta, Debo
Feddema, Diane
Fursin, Grigori
Guo, Junyi
Huang, Xinyuan
Kanter, David
Kashyap, Satyananda
Lane, Nicholas
Mallick, Indranil
Mascagni, Pietro
Mehta, Virendra
Natarajan, Vivek
Nikolov, Nikola
Padoy, Nicolas
Pekhimenko, Gennady
Reddi, Vijay Janapa
Reina, G Anthony
Ribalta, Pablo
Rosenthal, Jacob
Singh, Abhishek
Thiagarajan, Jayaraman J.
Wuest, Anna
Xenochristou, Maria
Xu, Daguang
Yadav, Poonam
Rosenthal, Michael
Loda, Massimo
Johnson, Jason M.
Mattson, Peter
Publication Year :
2021
Publisher :
arXiv, 2021.

Abstract

Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience. We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. To meet this need, we are building MedPerf, an open framework for benchmarking machine learning in the medical domain. MedPerf will enable federated evaluation in which models are securely distributed to different facilities for evaluation, thereby empowering healthcare organizations 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 our roadmap. We call for researchers and organizations to join us in creating the MedPerf open benchmarking platform.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....f99d5cef90aab7aa9106717743360579
Full Text :
https://doi.org/10.48550/arxiv.2110.01406