Accelerating artificial intelligence: How federated learning can protect privacy, facilitate collaboration, and improve outcomes

Author:

Patel Malhar1ORCID,Dayan Ittai1,Fishman Elliot K2ORCID,Flores Mona3,Gilbert Fiona J4,Guindy Michal5,Koay Eugene J6,Rosenthal Michael7,Roth Holger R3,Linguraru Marius G8

Affiliation:

1. Rhino Health, Boston, MA, USA

2. The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA

3. NVIDIA, Santa Clara, CA, USA

4. Department of Radiology, NIHR Cambridge Biomedical Resource Centre, University of Cambridge, Cambridge, CB, USA

5. Assuta Medical Centers, Tel Aviv, Israel; BGU University Israel, Beer-Sheva, Israel

6. Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

7. Dana-Farber Cancer Institute, Boston, MA, USA; Brigham & Women’s Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA

8. Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC, USA; Departments of Radiology and Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA

Abstract

Cross-institution collaborations are constrained by data-sharing challenges. These challenges hamper innovation, particularly in artificial intelligence, where models require diverse data to ensure strong performance. Federated learning (FL) solves data-sharing challenges. In typical collaborations, data is sent to a central repository where models are trained. With FL, models are sent to participating sites, trained locally, and model weights aggregated to create a master model with improved performance. At the 2021 Radiology Society of North America’s (RSNA) conference, a panel was conducted titled “Accelerating AI: How Federated Learning Can Protect Privacy, Facilitate Collaboration and Improve Outcomes.” Two groups shared insights: researchers from the EXAM study (EMC CXR AI Model) and members of the National Cancer Institute’s Early Detection Research Network’s (EDRN) pancreatic cancer working group. EXAM brought together 20 institutions to create a model to predict oxygen requirements of patients seen in the emergency department with COVID-19 symptoms. The EDRN collaboration is focused on improving outcomes for pancreatic cancer patients through earlier detection. This paper describes major insights from the panel, including direct quotes. The panelists described the impetus for FL, the long-term potential vision of FL, challenges faced in FL, and the immediate path forward for FL.

Publisher

SAGE Publications

Subject

Health Informatics

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3