Evidence map›Paper›PMID 34902546›Full record

ReviewProgress in retinal and eye research2022

Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice.

Cristina González-Gonzalo, Eric F Thee, Caroline C W Klaver, Aaron Y Lee, Reinier O Schlingemann, Adnan Tufail, Frank Verbraak, Clara I Sánchez

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Progress in retinal and eye research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
–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

32 citing papers in PubMed.

  1. Review
  2. Comparative evaluation of large language models and clinicians in real-world glaucoma clinical reasoning.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Article
  3. Article
  4. Review
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  8. Artificial intelligence for age-related macular degeneration diagnosis in Australia: A Novel Qualitative Interview Study.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025
    Article
  9. Article
  10. Article
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  12. Review
  13. Article
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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

8 authors.

Cristina González-GonzaloEye Lab, qurAI Group, Informatics Institute, University of Amsterdam, Amsterdam, the Netherlands; Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands. Electronic address: c.gonzalezgonzalo@uva.nl.
Eric F TheeDepartment of Ophthalmology, Erasmus Medical Center, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.
Caroline C W KlaverDepartment of Ophthalmology, Erasmus Medical Center, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands; Department of Ophthalmology, Radboud University Medical Center, Nijmegen, the Netherlands; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland.
Aaron Y LeeDepartment of Ophthalmology, School of Medicine, University of Washington, Seattle, WA, USA.
Reinier O SchlingemannDepartment of Ophthalmology, Amsterdam University Medical Center, Amsterdam, the Netherlands; Department of Ophthalmology, University of Lausanne, Jules Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland.
Adnan TufailMoorfields Eye Hospital NHS Foundation Trust, London, United Kingdom; Institute of Ophthalmology, University College London, London, United Kingdom.
Frank VerbraakDepartment of Ophthalmology, Amsterdam University Medical Center, Amsterdam, the Netherlands.
Clara I SánchezEye Lab, qurAI Group, Informatics Institute, University of Amsterdam, Amsterdam, the Netherlands; Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, the Netherlands.

Funding

Translational pharmacoepidemiology: neuroprotection and neurotoxicity of antihypertensives and strong anticholinergicsU19AG066567 · NIA · KAISER FOUNDATION RESEARCH INSTITUTE · PI Paul K Crane, Andrea Z. LaCroix · 2021 to 2026
$80.4M
Aging eyes and aging brains in studying alzheimer's disease: Modern ophthalmic data collection in the adult changes in thought (ACT) studyR01AG060942 · NIA · WASHINGTON UNIVERSITY · PI Cecilia Sungmin Lee · 2019 to 2026
$39.4M
Epidemiology and clinical outcomes of diabetic macular edemaK23EY029246 · NEI · UNIVERSITY OF WASHINGTON · PI LEE, AARON · 2018 to 2022
$1.1M
NEI NIH HHS K23 EY029246NIA NIH HHS R01 AG060942NIA NIH HHS U19 AG066567
6 · The paper itself

Abstract

An increasing number of artificial intelligence (AI) systems are being proposed in ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as their potential benefits at the different stages of patient care. Despite achieving close or even superior performance to that of experts, there is a critical gap between development and integration of AI systems in ophthalmic practice. This work focuses on the importance of trustworthy AI to close that gap. We identify the main aspects or challenges that need to be considered along the AI design pipeline so as to generate systems that meet the requirements to be deemed trustworthy, including those concerning accuracy, resiliency, reliability, safety, and accountability. We elaborate on mechanisms and considerations to address those aspects or challenges, and define the roles and responsibilities of the different stakeholders involved in AI for ophthalmic care, i.e., AI developers, reading centers, healthcare providers, healthcare institutions, ophthalmological societies and working groups or committees, patients, regulatory bodies, and payers. Generating trustworthy AI is not a responsibility of a sole stakeholder. There is an impending necessity for a collaborative approach where the different stakeholders are represented along the AI design pipeline, from the definition of the intended use to post-market surveillance after regulatory approval. This work contributes to establish such multi-stakeholder interaction and the main action points to be taken so that the potential benefits of AI reach real-world ophthalmic settings.

Indexed as

Artificial IntelligenceOphthalmologyDelivery of Health CareHumansReproducibility of ResultsArtificial intelligenceDeep learningIntegrationMachine learningOphthalmic careTrustworthiness

Identifiers

PMID34902546
PMCPMC11696120

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.