Evidence map›Paper›PMID 34478784›Full record

ArticleOphthalmology2022

Foundational Considerations for Artificial Intelligence Using Ophthalmic Images.

Michael D Abràmoff, Brad Cunningham, Bakul Patel, Malvina B Eydelman, Theodore Leng, Taiji Sakamoto, Barbara Blodi, S Marlene Grenon, Risa M Wolf, Arjun K Manrai and 4 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 3 pooled it
8.5field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

58 citing papers in PubMed, 3 syntheses or guidelines pooled it, 86 citations in OpenAlex.

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  6. Ethics of Autonomous AI Clinical Trials: Delphi Study.Journal of medical Internet research · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 9 institutions in 2 countries.

Michael D AbràmoffDepartment 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.
Brad CunninghamCenter for Devices and Radiological Health, Office of Health Technology 1, United States Food and Drug Administration, Silver Springs, Maryland.
Bakul PatelCenter for Devices and Radiological Health, Digital Health Center of Excellence, United States Food and Drug Administration, Silver Springs, Maryland.
Malvina B EydelmanCenter for Devices and Radiological Health, Office of Health Technology 1, United States Food and Drug Administration, Silver Springs, Maryland.
Theodore LengByers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California.
Taiji SakamotoDepartment of Ophthalmology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan; Japanese Vitreous Retina Society, Osaka, Japan.
Barbara BlodiDepartment of Ophthalmology, University of Wisconsin, Madison, Wisconsin.
S Marlene GrenonInnovation Ventures, University of California, San Francisco, San Francisco, California; Division of Vascular and Endovascular Surgery, Universify of California San Francisco, California.
Risa M WolfDepartment of Pediatric Endocrinology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Arjun K ManraiComputational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts.
Justin M KoDepartment of Dermatology, Stanford University School of Medicine, Stanford, California.
Michael F ChiangNational Eye Institute, Bethesda, Maryland.
Danton CharDivision 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.
Collaborative Community on Ophthalmic Imaging Executive Committee and Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group
United States Food and Drug Administration · USNational Eye Institute · USStanford University · USUniversity of California, San Francisco · USUniversity of Iowa · USJohns Hopkins University · USKagoshima University · JPUniversity of Wisconsin–Madison · USBoston Children's Hospital · US

Funding

Novel Glaucoma Diagnostics for Structure and Function - Renewal - 1R01EY013178 · NEI · WILLS EYE HEALTH SYSTEM · PI Joel S Schuman · 2000 to 2026
$17.5M
Stanford Vision Research CoreP30EY026877 · NEI · STANFORD UNIVERSITY · PI Jeffrey L Goldberg · 2017 to 2026
$8.0M
NEI NIH HHS P30 EY026877NEI NIH HHS R01 EY013178
6 · The paper itself

Abstract

importanceThe 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.

objectivesTo 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.

findingsArtificial 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.

Indexed as

Artificial IntelligenceDiagnostic ImagingOptical ImagingBioethicsEye DiseasesHumansSoftwareTranslational Research, BiomedicalArtificial intelligenceAugmented intelligenceautonomyclinical outcomeClinical standardsClinical trialCorneaequityEthicsexplainabilityFDAGlaucomahealth disparitiesimagingnon-maleficenceOculoplasticspatient benefitpopulation achieved sensitivitypopulation healthRegulationRetinaSafetyscalabilitytransparencyvalidabilityvalidationvernacular medicine

Identifiers

PMID34478784
PMCPMC9175066
OpenAlexW3198667874

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.