Leveraging physiology and artificial intelligence to deliver advancements in health care
Year: 2023
Type: review
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.
Source: Physiological Reviews
Authors: Angela Zhang, Zhenqin Wu, Eric Wu, Matthew Wu, Michael P. Snyder, James Zou, Joseph C. Wu
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Using Deep Learning to Predict Induced Pluripotent Stem Cell-Derived Cardiomyocyte (iPSC-CM) Differentiation Outcomes, CAREER: Enabling data valuation and deletion in human-centered machine learning, Genome Editing of Human iPSCs to Study Inherited Hypertrophic Cardiomyopathy, G8146812410, Role of Single Cell mRNA Variation in Systems Associated Electrically Excitable C, Stanford Medicine Center for Longevity and Healthy Aging, Elucidating ECM Signaling in Cardiac Organoids with Machine Learning and Single-cell Multiomics, Elucidating Mechanism of Cardiac Fibrosis with Cell Village of Pooled Human iPSCs, Human iPSC Model to Elucidate Metabolic Interplay in Diabetic Cardimyopathy
OpenAlex ID: W4367173237