Evidence map›Paper›PMID 34383720›Full record

ReviewAsia-Pacific journal of ophthalmology (Philadelphia, Pa.)

Ethics of Artificial Intelligence in Medicine and Ophthalmology.

Yasser Ibraheem Abdullah, Joel S Schuman, Ridwan Shabsigh, Arthur Caplan, Lama A Al-Aswad

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Asia-Pacific journal of ophthalmology (Philadelphia, Pa.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

59 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Yasser Ibraheem AbdullahDepartment of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY.
Joel S SchumanDepartment of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY.
Ridwan ShabsighSBH Health System and Weill Cornell Medical College, New York, NY.
Arthur CaplanDepartment of Population Health, NYU Langone Health, NYU Grossman School of Medicine, New York, NY.
Lama A Al-AswadDepartment of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY.

Funding

South Texas Alzheimer's Disease Research CenterP30AG066546 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Sudha Seshadri · 2021 to 2026
$24.0M
Manhattan Vision Screening and Follow-Up Study in Vulnerable PopulationsU01DP006436 · DP · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HARK, LISA A. · 2019 to 2023
$3.5M
HEY: Hispanic Eye StudyR21EY029605 · NEI · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI AL-ASWAD, LAMA A, MAESTRE, GLADYS E. · 2018 to 2019
$352k
NEI NIH HHS R21 EY029605NIA NIH HHS P30 AG066546
6 · The paper itself

Abstract

backgroundThis review explores the bioethical implementation of artificial intelligence (AI) in medicine and in ophthalmology. AI, which was first introduced in the 1950s, is defined as "the machine simulation of human mental reasoning, decision making, and behavior". The increased power of computing, expansion of storage capacity, and compilation of medical big data helped the AI implementation surge in medical practice and research. Ophthalmology is a leading medical specialty in applying AI in screening, diagnosis, and treatment. The first Food and Drug Administration approved autonomous diagnostic system served to diagnose and classify diabetic retinopathy. Other ophthalmic conditions such as age-related macular degeneration, glaucoma, retinopathy of prematurity, and congenital cataract, among others, implemented AI too. PURPOSE: To review the contemporary literature of the bioethical issues of AI in medicine and ophthalmology, classify ethical issues in medical AI, and suggest possible standardizations of ethical frameworks for AI implementation.

methodsKeywords were searched on Google Scholar and PubMed between October 2019 and April 2020. The results were reviewed, cross-referenced, and summarized. A total of 284 references including articles, books, book chapters, and regulatory reports and statements were reviewed, and those that were relevant were cited in the paper.

resultsMost sources that studied the use of AI in medicine explored the ethical aspects. Bioethical challenges of AI implementation in medicine were categorized into 6 main categories. These include machine training ethics, machine accuracy ethics, patient-related ethics, physician-related ethics, shared ethics, and roles of regulators.

conclusionsThere are multiple stakeholders in the ethical issues surrounding AI in medicine and ophthalmology. Attention to the various aspects of ethics related to AI is important especially with the expanding use of AI. Solutions of ethical problems are envisioned to be multifactorial.

Indexed as

Artificial IntelligenceOphthalmologyDiabetic RetinopathyGlaucomaHumans

Identifiers

PMID34383720
PMCPMC9167644

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.