Evidence map›Paper›PMID 38483456›Full record

ArticleJMIR research protocols2024

AI as a Medical Device for Ophthalmic Imaging in Europe, Australia, and the United States: Protocol for a Systematic Scoping Review of Regulated Devices.

Ariel Yuhan Ong, Henry David Jeffry Hogg, Aditya U Kale, Priyal Taribagil, Ashley Kras, Eliot Dow, Trystan Macdonald, Xiaoxuan Liu, Pearse A Keane, Alastair K Denniston

Open access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
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

10 authors at 4 institutions in 3 countries.

Ariel Yuhan OngMoorfields Eye Hospital NHS Foundation Trust, London, United Kingdom.ORCID https://orcid.org/0000-0001-9300-573X
Henry David Jeffry HoggMoorfields Eye Hospital NHS Foundation Trust, London, United Kingdom.ORCID https://orcid.org/0000-0001-8044-7790
Aditya U KaleDepartment of Ophthalmology, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.ORCID https://orcid.org/0000-0002-2186-1446
Priyal TaribagilMoorfields Eye Hospital NHS Foundation Trust, London, United Kingdom.ORCID https://orcid.org/0000-0002-6516-3615
Ashley KrasSydney Eye Hospital, Sydney, Australia.ORCID https://orcid.org/0000-0003-1160-104X
Eliot DowRetinal Consultants Medical Group, Sacramento, CA, United States.ORCID https://orcid.org/0000-0001-7409-2049
Trystan MacdonaldDepartment of Ophthalmology, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.ORCID https://orcid.org/0000-0002-8561-0718
Xiaoxuan LiuDepartment of Ophthalmology, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.ORCID https://orcid.org/0000-0002-1286-0038
Pearse A KeaneMoorfields Eye Hospital NHS Foundation Trust, London, United Kingdom.ORCID https://orcid.org/0000-0002-9239-745X
Alastair K DennistonDepartment of Ophthalmology, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.ORCID https://orcid.org/0000-0001-7849-0087
Moorfields Eye Hospital NHS Foundation Trust · GBUniversity Hospitals Birmingham NHS Foundation Trust · GBCalifornia Retina Consultants · USSydney Hospital · AU

Funding

Medical Research Council MC_PC_19005
6 · The paper itself

Abstract

backgroundArtificial intelligence as a medical device (AIaMD) has the potential to transform many aspects of ophthalmic care, such as improving accuracy and speed of diagnosis, addressing capacity issues in high-volume areas such as screening, and detecting novel biomarkers of systemic disease in the eye (oculomics). In order to ensure that such tools are safe for the target population and achieve their intended purpose, it is important that these AIaMD have adequate clinical evaluation to support any regulatory decision. Currently, the evidential requirements for regulatory approval are less clear for AIaMD compared to more established interventions such as drugs or medical devices. There is therefore value in understanding the level of evidence that underpins AIaMD currently on the market, as a step toward identifying what the best practices might be in this area. In this systematic scoping review, we will focus on AIaMD that contributes to clinical decision-making (relating to screening, diagnosis, prognosis, and treatment) in the context of ophthalmic imaging.

objectiveThis study aims to identify regulator-approved AIaMD for ophthalmic imaging in Europe, Australia, and the United States; report the characteristics of these devices and their regulatory approvals; and report the available evidence underpinning these AIaMD.

methodsThe Food and Drug Administration (United States), the Australian Register of Therapeutic Goods (Australia), the Medicines and Healthcare products Regulatory Agency (United Kingdom), and the European Database on Medical Devices (European Union) regulatory databases will be searched for ophthalmic imaging AIaMD through a snowballing approach. PubMed and clinical trial registries will be systematically searched, and manufacturers will be directly contacted for studies investigating the effectiveness of eligible AIaMD. Preliminary regulatory database searches, evidence searches, screening, data extraction, and methodological quality assessment will be undertaken by 2 independent review authors and arbitrated by a third at each stage of the process.

resultsPreliminary searches were conducted in February 2023. Data extraction, data synthesis, and assessment of methodological quality commenced in October 2023. The review is on track to be completed and submitted for peer review by April 2024.

conclusionsThis systematic review will provide greater clarity on ophthalmic imaging AIaMD that have achieved regulatory approval as well as the evidence that underpins them. This should help adopters understand the range of tools available and whether they can be safely incorporated into their clinical workflow, and it should also support developers in navigating regulatory approval more efficiently. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52602.

Indexed as

AIaMDartificial intelligenceartificial intelligence as a medical devicedeep learningmachine learningophthalmic imagingregulatory approval

Identifiers

PMID38483456
PMCPMC10979335
OpenAlexW4391975831

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.