Autonomous Artificial Intelligence in Diabetic Retinopathy: From Algorithm to Clinical Application

Roomasa Channa; Risa Wolf; Michael D Abramoff

doi:10.1177/1932296820909900

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Autonomous Artificial Intelligence in Diabetic Retinopathy: From Algorithm to Clinical Application

Journal article

Open access

Peer reviewed

Autonomous Artificial Intelligence in Diabetic Retinopathy: From Algorithm to Clinical Application

Roomasa Channa, Risa Wolf and Michael D Abramoff

Journal of diabetes science and technology, Vol.15(3), pp.695-698

2021

DOI: 10.1177/1932296820909900

PMID: 32126819

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Abstract

Artificial intelligence (AI)-based algorithms are rapidly entering the health care field and have the potential to improve patient care. Our article focuses on the use of autonomous AI algorithms (ie, algorithms that can make clinical decisions without human oversight) in diagnostic imaging. In this article, we have used the example of diabetic retinopathy screening to highlight some important aspects to be considered by developers, policymakers, and end users when bringing autonomous AI algorithms into clinical practice. We have divided these aspects into (1) following the principles of safety, efficacy, and equity in all phases of development and implementation of the algorithm; (2) regulatory processes involving medical records, medical liability, and patient privacy; (3) cost and billing; and (4) the role of health care providers.

Details

Title: Subtitle: Autonomous Artificial Intelligence in Diabetic Retinopathy: From Algorithm to Clinical Application
Creators: Roomasa Channa - Department of Ophthalmology, Baylor College of Medicine, Houston, TX, USA, Michael DeBakey Veterans Affairs Hospital, Houston, TX, USA, Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD, USA
Risa Wolf - Pediatric Endocrinology, Johns Hopkins University, Baltimore, MD, USA
Michael D Abramoff - The Robert C. Watzke Professor of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USA, VA Medical Center, Iowa City, IA, USA, IDx, Coralville, IA, USA
Resource Type: Journal article
Publication Details: Journal of diabetes science and technology, Vol.15(3), pp.695-698
DOI: 10.1177/1932296820909900
PMID: 32126819
NLM abbreviation: J Diabetes Sci Technol
ISSN: 1932-2968
eISSN: 1932-2968
Language: English
Date published: 2021
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Fraternal Order of Eagles Diabetes Research Center; Ophthalmology and Visual Sciences
Record Identifier: 9984060974602771

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