Leveraging physiology and artificial intelligence to deliver advancements in health care

Author:

Zhang Angela123,Wu Zhenqin4,Wu Eric5ORCID,Wu Matthew3,Snyder Michael P.2ORCID,Zou James67,Wu Joseph C.1389ORCID

Affiliation:

1. Stanford Cardiovascular Institute, School of Medicine, Stanford University, Stanford, California, United States

2. Department of Genetics, School of Medicine, Stanford University, Stanford, California, United States

3. Greenstone Biosciences, Palo Alto, California, United States

4. Department of Chemistry, Stanford University, Stanford, California, United States

5. Department of Electrical Engineering, Stanford University, Stanford, California, United States

6. Department of Biomedical Informatics, School of Medicine, Stanford University, Stanford, California, United States

7. Department of Computer Science, Stanford University, Stanford, California, United States

8. Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, United States

9. Department of Radiology, School of Medicine, Stanford University, Stanford, California, United States

Abstract

Artificial intelligence in health care has experienced remarkable innovation and progress in the last decade. Significant advancements can be attributed to the utilization of artificial intelligence to transform physiology data to advance health care. In this review, we explore how past work has shaped the field and defined future challenges and directions. In particular, we focus on three areas of development. First, we give an overview of artificial intelligence, with special attention to the most relevant artificial intelligence models. We then detail how physiology data have been harnessed by artificial intelligence to advance the main areas of health care: automating existing health care tasks, increasing access to care, and augmenting health care capabilities. Finally, we discuss emerging concerns surrounding the use of individual physiology data and detail an increasingly important consideration for the field, namely the challenges of deploying artificial intelligence models to achieve meaningful clinical impact.

Funder

HHS | National Institutes of Health

National Science Foundation

HHS | NIH | National Heart, Lung, and Blood Institute

American Heart Association

Publisher

American Physiological Society

Subject

Physiology (medical),Molecular Biology,Physiology,General Medicine

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