Artificial intelligence-enhanced patient evaluation: bridging art and science

Author:

Oikonomou Evangelos K1ORCID,Khera Rohan1234ORCID

Affiliation:

1. Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine , 333 Cedar Street, PO Box 208017, New Haven, 06520-8017 CT , USA

2. Center for Outcomes Research and Evaluation, Yale New Haven Hospital , New Haven, 195 Church St, 6th Floor, New Haven, CT 06510 , USA

3. Section of Biomedical Informatics and Data Science, Yale School of Medicine , 100 College Street, New Haven, 06511 CT , USA

4. Section of Health Informatics, Department of Biostatistics, Yale School of Public Health , 60 College Street, New Haven, 06510 CT , USA

Abstract

Abstract The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic’s view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

Funder

National Institutes of Health

Doris Duke Charitable Foundation

Publisher

Oxford University Press (OUP)

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