Evidence map›Paper›PMID 39992600›Full record

ReviewWiener medizinische Wochenschrift (1946)2025

Artificial intelligence in managing retinal disease-current concepts and relevant aspects for health care providers.

Sophie Riedl, Klaudia Birner, Ursula Schmidt-Erfurth

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Wiener medizinische Wochenschrift (1946), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Sophie RiedlDepartment of Ophthalmology and Optometry, Laboratory of Ophthalmic Image Analysis, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
Klaudia BirnerDepartment of Ophthalmology and Optometry, Laboratory of Ophthalmic Image Analysis, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
Ursula Schmidt-ErfurthDepartment of Ophthalmology and Optometry, Laboratory of Ophthalmic Image Analysis, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria. ursula.schmidt-erfurth@meduniwien.ac.at.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Given how the diagnosis and management of many ocular and, most specifically, retinal diseases heavily rely on various imaging modalities, the introduction of artificial intelligence (AI) into this field has been a logical, inevitable, and successful development in recent decades. The field of retinal diseases has practically become a showcase for the use of AI in medicine. In this article, after providing a short overview of the most relevant retinal diseases and their socioeconomic impact, we highlight various aspects of how AI can be applied in research, diagnosis, and disease management and how this is expected to alter patient flows, affecting also health care professionals beyond ophthalmologists.

Indexed as

Artificial IntelligenceRetinal DiseasesHumansArtificial intelligenceImagingOptical coherence tomographyRetinal disease

Identifiers

PMID39992600
PMCPMC12031981

What OpenQuestion holds

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Registered trials

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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.