Journal article
Lessons Learned About Autonomous AI: Finding a Safe, Efficacious, and Ethical Path Through the Development Process
American journal of ophthalmology, Vol.214, pp.134-142
06/2020
DOI: 10.1016/j.ajo.2020.02.022
PMID: 32171769
Abstract
Artificial intelligence (AI) describes systems capable of making decisions of high cognitive complexity; autonomous AI systems in healthcare are AI systems that make clinical decisions without human oversight. Such rigorously validated medical diagnostic AI systems hold great promise for improving access to care, increasing accuracy, and lowering cost, while enabling specialist physicians to provide the greatest value by managing and treating patients whose outcomes can be improved. Ensuring that autonomous AI provides these benefits requires evaluation of the autonomous AI's effect on patient outcome, design, validation, data usage, and accountability, from a bioethics and accountability perspective. We performed a literature review of bioethical principles for AI, and derived evaluation rules for autonomous AI, grounded in bioethical principles. The rules include patient outcome, validation, reference standard, design, data usage, and accountability for medical liability. Application of the rules explains successful US Food and Drug Administration (FDA) de novo authorization of an example, the first autonomous point-of-care diabetic retinopathy examination de novo authorized by the FDA, after a preregistered clinical trial. Physicians need to become competent in understanding the potential risks and benefits of autonomous AI, and understand its design, safety, efficacy and equity, validation, and liability, as well as how its data were obtained. The autonomous AI evaluation rules introduced here can help physicians understand limitations and risks as well as the potential benefits of autonomous AI for their patients.
Details
- Title: Subtitle
- Lessons Learned About Autonomous AI: Finding a Safe, Efficacious, and Ethical Path Through the Development Process
- Creators
- Michael D Abràmoff - Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, USADanny Tobey - DLA Piper, Dallas, Texas, USADanton S Char - Division of Pediatric Cardiac Anesthesia, Department of Anesthesiology, Stanford University School of Medicine, San Francisco, California, USA
- Resource Type
- Journal article
- Publication Details
- American journal of ophthalmology, Vol.214, pp.134-142
- DOI
- 10.1016/j.ajo.2020.02.022
- PMID
- 32171769
- NLM abbreviation
- Am J Ophthalmol
- ISSN
- 0002-9394
- eISSN
- 1879-1891
- Publisher
- Elsevier Inc
- Grant note
- name: IDx; DOI: 10.13039/100001818, name: Research to Prevent Blindness; DOI: 10.13039/100000053, name: National Eye Institute, award: R01 EY016822
- Language
- English
- Date published
- 06/2020
- 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
- 9984060762002771
Metrics
28 Record Views