Evidence map›Paper›PMID 41795058›Full record

ReviewOphthalmology and therapy2026

Artificial Intelligence in Vitreoretinal Surgery: A Systematic Review of Current Applications and Future Directions.

Enrico Bernardi, Lorenzo Ferro Desideri, Neil Shah, Carla Troyas, Ben Kirkpatrick, Yousif Subhi, Martin Zinkernagel, Rodrigo Anguita

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Ophthalmology and therapy, 2026. 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

8 authors.

Enrico BernardiDepartment of Ophthalmology, Belfast Health and Social Care Trust, Belfast, Northern Ireland, UK.
Lorenzo Ferro DesideriDepartment of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 15, CH-3010, Bern, Switzerland.
Neil ShahMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Carla TroyasMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Ben KirkpatrickMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Yousif SubhiDepartment of Ophthalmology, Rigshospitalet, Copenhagen, Denmark.
Martin ZinkernagelDepartment of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 15, CH-3010, Bern, Switzerland.
Rodrigo AnguitaMoorfields Eye Hospital NHS Foundation Trust, London, UK. rodrigoanguita@gmail.com.ORCID http://orcid.org/0000-0003-2382-1111

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTo examine the current landscape of artificial intelligence (AI) applications in vitreoretinal (VR) diseases and surgery, with the aim of identifying knowledge gaps and guiding future directions in this rapidly evolving field.

methodsSystematic review including original studies involving the use of AI and focusing on VR pathologies. A comprehensive electronic search of the literature was carried out in multiple databases.

resultsThirty-seven studies were included. Most evaluated machine learning or deep learning models for preoperative prognostication using optical coherence tomography with or without clinical variables. Predictive performance for postoperative best-corrected visual acuity (BCVA) was high in several cohorts (R

conclusionsAI may transform vitreoretinal surgery, from outcome prediction to intraoperative guidance and workflow support. Despite strong performance in research settings, broader clinical adoption requires prospective validation to ensure reliability, transparency, and real-world benefit.

Indexed as

Artificial intelligenceERMLarge language modelsMachine learningMacular holeMultimodal language modelsPVRRetinal detachmentVitrectomyVitreoretinal surgery

Identifiers

PMID41795058
PMCPMC13046929

What Socratic holds

Textmetadata
LicenceCC BY-NC
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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 Socratic graph.