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Federated benchmarking of medical artificial intelligence with MedPerf.
Karargyris, Alexandros; Umeton, Renato; Sheller, Micah J; Aristizabal, Alejandro; George, Johnu; Wuest, Anna; Pati, Sarthak; Kassem, Hasan; Zenk, Maximilian; Baid, Ujjwal; Narayana Moorthy, Prakash; Chowdhury, Alexander; Guo, Junyi; Nalawade, Sahil; Rosenthal, Jacob; Kanter, David; Xenochristou, Maria; Beutel, Daniel J; Chung, Verena; Bergquist, Timothy; Eddy, James; Abid, Abubakar; Tunstall, Lewis; Sanseviero, Omar; Dimitriadis, Dimitrios; Qian, Yiming; Xu, Xinxing; Liu, Yong; Goh, Rick Siow Mong; Bala, Srini; Bittorf, Victor; Reddy Puchala, Sreekar; Ricciuti, Biagio; Samineni, Soujanya; Sengupta, Eshna; Chaudhari, Akshay; Coleman, Cody; Desinghu, Bala; Diamos, Gregory; Dutta, Debo; Feddema, Diane; Fursin, Grigori; Huang, Xinyuan; Kashyap, Satyananda; Lane, Nicholas; Mallick, Indranil; Mascagni, Pietro; Mehta, Virendra; Ferro Moraes, Cassiano; Natarajan, Vivek.
Afiliación
  • Karargyris A; IHU Strasbourg, Strasbourg, France.
  • Umeton R; University of Strasbourg, Strasbourg, France.
  • Sheller MJ; These authors contributed equally: Alexandros Karargyris, Renato Umeton, Micah J. Sheller.
  • Aristizabal A; Dana-Farber Cancer Institute, Boston, MA, USA.
  • George J; Weill Cornell Medicine, New York, NY, USA.
  • Wuest A; Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Pati S; Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Kassem H; These authors contributed equally: Alexandros Karargyris, Renato Umeton, Micah J. Sheller.
  • Zenk M; Intel, Santa Clara, CA, USA.
  • Baid U; These authors contributed equally: Alexandros Karargyris, Renato Umeton, Micah J. Sheller.
  • Narayana Moorthy P; Factored, Palo Alto, CA, USA.
  • Chowdhury A; Nutanix, San Jose, CA, USA.
  • Guo J; Dana-Farber Cancer Institute, Boston, MA, USA.
  • Nalawade S; Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Rosenthal J; Perelman School of Medicine, Philadelphia, PA, USA.
  • Kanter D; University of Pennsylvania, Philadelphia, PA, USA.
  • Xenochristou M; University of Strasbourg, Strasbourg, France.
  • Beutel DJ; German Cancer Research Center, Heidelberg, Germany.
  • Chung V; University of Heidelberg, Heidelberg, Germany.
  • Bergquist T; Perelman School of Medicine, Philadelphia, PA, USA.
  • Eddy J; University of Pennsylvania, Philadelphia, PA, USA.
  • Abid A; Intel, Santa Clara, CA, USA.
  • Tunstall L; Dana-Farber Cancer Institute, Boston, MA, USA.
  • Sanseviero O; Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Dimitriadis D; Dana-Farber Cancer Institute, Boston, MA, USA.
  • Qian Y; Dana-Farber Cancer Institute, Boston, MA, USA.
  • Xu X; Weill Cornell Medicine, New York, NY, USA.
  • Liu Y; MLCommons, San Francisco, CA, USA.
  • Goh RSM; Stanford University, Stanford, CA, USA.
  • Bala S; University of Cambridge, Cambridge, UK.
  • Bittorf V; Flower Labs, Hamburg, Germany.
  • Reddy Puchala S; Sage Bionetworks, Seattle, WA, USA.
  • Ricciuti B; Sage Bionetworks, Seattle, WA, USA.
  • Samineni S; Sage Bionetworks, Seattle, WA, USA.
  • Sengupta E; Hugging Face, New York, NY, USA.
  • Chaudhari A; Hugging Face, New York, NY, USA.
  • Coleman C; Hugging Face, New York, NY, USA.
  • Desinghu B; Microsoft, Redmond, WA, USA.
  • Diamos G; A*STAR, Singapore, Singapore.
  • Dutta D; A*STAR, Singapore, Singapore.
  • Feddema D; A*STAR, Singapore, Singapore.
  • Fursin G; A*STAR, Singapore, Singapore.
  • Huang X; Supermicro, San Jose, CA, USA.
  • Kashyap S; Meta, Menlo Park, CA, USA.
  • Lane N; Dana-Farber Cancer Institute, Boston, MA, USA.
  • Mallick I; Dana-Farber Cancer Institute, Boston, MA, USA.
  • Mascagni P; Stanford University School of Medicine, Stanford, CA, USA.
  • Mehta V; Stanford University, Stanford, CA, USA.
  • Ferro Moraes C; Rutgers University, New Brunswick, NJ, USA.
  • Natarajan V; Landing.AI, Palo Alto, CA, USA.
Nat Mach Intell ; 5(7): 799-810, 2023 Jul.
Article en En | MEDLINE | ID: mdl-38706981
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.

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Nat Mach Intell Año: 2023 Tipo del documento: Article País de afiliación: Francia

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Nat Mach Intell Año: 2023 Tipo del documento: Article País de afiliación: Francia