Evidence map›Paper›PMID 41006661›Full record

ArticleScientific reports2025

Evaluating the clinical utility of multimodal large language models for detecting age-related macular degeneration from retinal imaging.

Jesse A Most, Gillian A Folk, Evan H Walker, Ines D Nagel, Nehal N Mehta, Elena Flester, Shyamanga Borooah

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

7 authors.

Jesse A MostShiley Eye Institute, Jacobs Retina Center, University of California San Diego, La Jolla, CA, USA.
Gillian A FolkSchool of Medicine, University of California San Diego, La Jolla, CA, 92037, USA.
Evan H WalkerViterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, USA.
Ines D NagelShiley Eye Institute, Jacobs Retina Center, University of California San Diego, La Jolla, CA, USA.
Nehal N MehtaShiley Eye Institute, Jacobs Retina Center, University of California San Diego, La Jolla, CA, USA.
Elena FlesterSchool of Medicine, University of California San Diego, La Jolla, CA, 92037, USA.
Shyamanga BorooahShiley Eye Institute, Jacobs Retina Center, University of California San Diego, La Jolla, CA, USA. sborooah@health.ucsd.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This single-center retrospective study evaluated the performance of four multimodal large language models (MLLMs) (ChatGPT-4o, Claude 3.5 Sonnet, Google Gemini 1.5 Pro, Perplexity Sonar Large) in detecting and grading the severity of age-related macular degeneration (AMD) from ultrawide field fundus images. Images from 76 patients (136 eyes; mean age 81.1 years; 69.7% female) seen at the University of California San Diego were graded independently for AMD severity by two junior retinal specialists (and an adjudicating senior retina specialist for disagreements) using the Age-Related Eye Disease Study (AREDS) classification. The cohort included 17 (12.5%) eyes with 'No AMD', 18 (13.2%) with 'Early AMD', 50 (36.8%) with 'Intermediate AMD', and 51 (37.5%) with 'Advanced AMD'. Between December 2024 and February 2025, each MLLM was prompted with single images and standardized queries to assess the primary outcomes of accuracy, sensitivity, and specificity in binary disease classification, disease severity grading, open-ended diagnosis, and multiple-choice diagnosis (with distractor diseases). Secondary outcomes included precision, F1 scores, Cohen's kappa, model performance comparisons, and error analysis. ChatGPT-4o demonstrated the highest accuracy for binary disease classification [mean 0.824 (95% confidence interval (CI)): 0.743, 0.875)], followed by Perplexity Sonar Large [mean 0.815 (95% CI: 0.744, 0.879)], both of which were significantly more accurate (P < 0.00033) Than Gemini 1.5 Pro [mean 0.669 (95% CI: 0.581, 0.743)] and Claude 3.5 Sonnet [mean 0.301 (95% CI: 0.221, 0.375)]. For severity grading, Perplexity Sonar Large was most accurate [mean 0.463 (95% CI: 0.368, 0.537)], though differences among models were not statistically significant. ChatGPT-4o led in open-ended and multiple-choice diagnostic tasks. In summary, while MLLMs show promise for automated AMD detection and grading from fundus images, their current reliability is insufficient for clinical application, highlighting the need for further model development and validation.

Indexed as

Macular DegenerationRetinaAgedAged, 80 and overFemaleHumansLarge Language ModelsMaleRetrospective StudiesSeverity of Illness IndexAge-related macular degenerationArtificial intelligenceDeep learningLarge language modelsMulti-modal large language modelsUltrawide field fundus

Identifiers

PMID41006661
PMCPMC12475039

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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.