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. 2022 Feb;129(2):e14-e32.
doi: 10.1016/j.ophtha.2021.08.023. Epub 2021 Aug 31.

Foundational Considerations for Artificial Intelligence Using Ophthalmic Images

Michael D Abràmoff  1 , Brad Cunningham  2 , Bakul Patel  3 , Malvina B Eydelman  2 , Theodore Leng  4 , Taiji Sakamoto  5 , Barbara Blodi  6 , S Marlene Grenon  7 , Risa M Wolf  8 , Arjun K Manrai  9 , Justin M Ko  10 , Michael F Chiang  11 , Danton Char  12 ; Collaborative Community on Ophthalmic Imaging Executive Committee and Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group
Collaborators, Affiliations

Foundational Considerations for Artificial Intelligence Using Ophthalmic Images

Michael D Abràmoff et al. Ophthalmology. 2022 Feb.

Abstract

Importance: The development of artificial intelligence (AI) and other machine diagnostic systems, also known as software as a medical device, 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.

Objectives: To 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.

Evidence review: 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 afterward in the working group.

Findings: Artificial intelligence has the potential to improve health care access and patient outcome fundamentally 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 and other regulatory agencies, industry, patient advocacy groups, clinicians and their professional societies, other provider groups, and payors (i.e., stakeholders) working together in collaborative communities to resolve the fundamental ethical issues of nonmaleficence, autonomy, and equity are 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.

Conclusions and relevance: The development of a bioethically sound foundation may be possible if it is based in the fundamental ethical principles of nonmaleficence, 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 health care, decrease in health disparities, and lower cost thereby can be achieved. These considerations should be discussed with all stakeholders and expanded on as a useful initiation of this dialogue.

Keywords: Artificial intelligence; Augmented intelligence; Clinical standards; Clinical trial; Cornea; Ethics; FDA; Glaucoma; Oculoplastics; Regulation; Retina; Safety; autonomy; clinical outcome; equity; explainability; health disparities; imaging; non-maleficence; patient benefit; population achieved sensitivity; population health; scalability; transparency; validability; validation; vernacular medicine.

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Figures

Figure 1.
Figure 1.
Diagram showing the balance and tension among the 3 bioethical principles: nonmaleficence, autonomy, and equity (justice).
See this image and copyright information in PMC

References

    1. Center for Devices and Radiological Health, United States Food and Drug Administration. Artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD) action plan. January 2021. Available at: https://www.fda.gov/media/145022/download. Accessed August 14, 2021.
    1. Stanford University. Collaborative community on ophthalmic imaging (CCOI). Available at: https://www.cc-oi.org/; 2020. Accessed August 14, 2021.
    1. Abramoff MD, Tobey D, Char DS. Lessons learned about autonomous AI: finding a safe, efficacious, and ethical path through the development process. Am J Ophthalmol. 2020;214(1):134–142. - PubMed
    1. Char DS, Abràmoff MD, Feudtner C. Identifying ethical considerations for machine learning healthcare applications. Am J Bioethics. 2020/11/01 2020;20(11):7–17. - PMC - PubMed
    1. Abramoff MD. The autonomous point of care diabetic retinopathy examination. In: Klonoff DC, Kerr D, Mulvaney SA, eds. Diabetes Digital Health. New York: Springer; 2020:55–67.

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