Michael D Abràmoff1, Brad Cunningham2, Bakul Patel3, Malvina B Eydelman2, Theodore Leng4, Taiji Sakamoto5, Barbara Blodi6, S Marlene Grenon7, Risa M Wolf8, Arjun K Manrai9, Justin M Ko10, Michael F Chiang11, Danton Char12. 1. Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa; Department of Elecrical and Computer Engineering, University of Iowa, Iowa City, Iowa; Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa. Electronic address: michael-abramoff@uiowa.edu. 2. Center for Devices and Radiological Health, Office of Health Technology 1, United States Food and Drug Administration, Silver Springs, Maryland. 3. Center for Devices and Radiological Health, Digital Health Center of Excellence, United States Food and Drug Administration, Silver Springs, Maryland. 4. Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California. 5. Department of Ophthalmology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan; Japanese Vitreous Retina Society, Osaka, Japan. 6. Department of Ophthalmology, University of Wisconsin, Madison, Wisconsin. 7. Innovation Ventures, University of California, San Francisco, San Francisco, California; Division of Vascular and Endovascular Surgery, Universify of California San Francisco, California. 8. Department of Pediatric Endocrinology, Johns Hopkins University School of Medicine, Baltimore, Maryland. 9. Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts. 10. Department of Dermatology, Stanford University School of Medicine, Stanford, California. 11. National Eye Institute, Bethesda, Maryland. 12. Division of Pediatric Cardiac Anesthesia, Department of Anesthesiology, Stanford University School of Medicine, San Francisco, California; Center for Biomedical Ethics, Stanford University School of Medicine, San Francisco, California.
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. Published by Elsevier Inc.
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. Published by Elsevier Inc.
Entities:
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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