Foundational Considerations for Artificial Intelligence Utilizing Ophthalmic Images

Michael D Abramoff; Brad Cunningham; Bakul Patel; Malvina B Eydelman; Theodore Leng; Taiji Sakamoto; Barbara Blodi; S. Marlene Grenon; Risa M Wolf; Arjun K Manrai; Justin M Ko; Michael F Chiang; Danton Char; Collaborative Community on Ophthalmic Imaging Executive Committee and Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group

doi:10.1016/j.ophtha.2021.08.023

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Foundational Considerations for Artificial Intelligence Utilizing Ophthalmic Images

Journal article

Peer reviewed

Foundational Considerations for Artificial Intelligence Utilizing Ophthalmic Images

Michael D Abramoff, Brad Cunningham, Bakul Patel, Malvina B Eydelman, Theodore Leng, Taiji Sakamoto, Barbara Blodi, S. Marlene Grenon, Risa M Wolf, Arjun K Manrai, …

Ophthalmology (Rochester, Minn.), Vol.129(2), pp.e14-e32

08/31/2021

DOI: 10.1016/j.ophtha.2021.08.023

PMCID: PMC9175066

PMID: 34478784

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https://www.ncbi.nlm.nih.gov/pmc/articles/9175066View

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Abstract

The development of Artificial Intelligence (AI) and other machine diagnostic systems, also known as Software as a Medical Device (SaMD), and its recent introduction into clinical practice, requires a deeply-rooted foundation in bioethics, for consideration by regulatory agencies and other stakeholders around the globe. Initiate a dialogue on the issues to consider when developing a bioethically sound foundation for AI in medicine, based on images of eye structures, for discussion with all stakeholders. The scope of the issues and summaries of the discussions under consideration by the Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group, as first presented during the Collaborative Community on Ophthalmic Imaging inaugural meeting on September 7, 2020, and afterwards in the working group. AI has the potential to fundamentally improve healthcare access and patient outcome, while decreasing disparities, lowering cost, and enhancing the care team. Nevertheless, substantial concerns exist. Bioethicists, AI algorithm experts, as well as the Food and Drug Administration (FDA) and other regulatory agencies, industry, patient advocacy groups, clinicians and their professional societies, other provider groups, payors, (“stakeholders”), working together in collaborative communities to resolve the fundamental ethical issues of non-maleficence, autonomy and equity, is essential to attain this potential. Resolution impacts all levels of the design, validation and implementation of AI in medicine. Design, validation and implementation of AI warrant meticulous attention. The development of a bioethically sound foundation may be possible if it is based in the fundamental ethical principles non-maleficence, autonomy and equity, for considerations for the design, validation and implementation for AI systems. Achieving such a foundation will be helpful for continuing successful introduction into medicine, before consideration by regulatory agencies. Important improvements in accessibility and quality of healthcare, decrease in health disparities, and lower cost can thereby be achieved. These considerations should be discussed with all stakeholders and expanded upon as a useful initiation of this dialogue.

Details

Title: Subtitle: Foundational Considerations for Artificial Intelligence Utilizing Ophthalmic Images
Creators: Michael D Abramoff - University of Iowa, Iowa City, IA
Brad Cunningham - Center for Devices and Radiological Health, Office of Health Technology-1, US Food and Drug Administration, Silver Springs, MD
Bakul Patel - Center for Devices and Radiological Health, Digital Health Center of Excellence, US Food and Drug Administration, Silver Springs, MD
Malvina B Eydelman - Center for Devices and Radiological Health, Office of Health Technology-1, US Food and Drug Administration, Silver Springs, MD
Theodore Leng - Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA
Taiji Sakamoto - Kagoshima University, Kyushu Pref, Japan
Barbara Blodi - Department of Ophthalmology, University of Wisconsin, Madison, WI
S. Marlene Grenon - Innovation Ventures, University of California San Francisco, San Francisco, CA
Risa M Wolf - Department of Pediatric Endocrinology, Johns Hopkins University School of Medicine, Baltimore, MD
Arjun K Manrai - Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA
Justin M Ko - Department of Dermatology, Stanford University School of Medicine, Stanford, CA
Michael F Chiang - National Eye Institute, Bethesda, MD
Danton Char - Department of Anesthesiology, Stanford University School of Medicine, Division of Pediatric Cardiac Anesthesia, San Francisco, CA
Collaborative Community on Ophthalmic Imaging Executive Committee and Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group
Resource Type: Journal article
Publication Details: Ophthalmology (Rochester, Minn.), Vol.129(2), pp.e14-e32
DOI: 10.1016/j.ophtha.2021.08.023
PMID: 34478784
PMCID: PMC9175066
NLM abbreviation: Ophthalmology
ISSN: 0161-6420
eISSN: 1549-4713
Publisher: Elsevier Inc
Grant note: DOI: 10.13039/100001818, name: Research to Prevent Blindness; DOI: 10.13039/100008893, name: University of Iowa
Language: English
Date published: 08/31/2021
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: 9984172263802771

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