Evidence map›Paper›PMID 41085353›Full record

ArticleTranslational vision science & technology2025

Assessing the Clinical Utility of Multimodal Large Language Models in the Diagnosis and Management of Pigmented Choroidal Lesions.

Nehal Nailesh Mehta, Evan Walker, Elena Flester, Gillian Folk, Akshay Agnihotri, Ines D Nagel, Melanie Tran, Michael H Goldbaum, Shyamanga Borooah, Nathan L Scott

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational vision science & technology, 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

10 authors.

Nehal Nailesh MehtaJacobs Retina Center, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Evan WalkerViterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Elena FlesterJacobs Retina Center, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Gillian FolkJacobs Retina Center, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Akshay AgnihotriJacobs Retina Center, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Ines D NagelJacobs Retina Center, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Melanie TranJacobs Retina Center, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Michael H GoldbaumJacobs Retina Center, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Shyamanga BorooahJacobs Retina Center, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Nathan L ScottJacobs Retina Center, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To evaluate the diagnostic and treatment recommendation performance of multimodal large language models (MLLMs) in identifying and classifying retinal lesions as choroidal nevus or melanoma, as well as compare their performance with expert human graders. Methods: This retrospective cross-sectional study included 48 eyes from 47 patients diagnosed with either choroidal nevus or melanoma. Patient demographics, including age, sex, ethnicity, best-corrected visual acuity (BCVA), and symptoms, were documented. Color fundus, autofluorescence, optical coherence tomography, and B-scan images were collected. The ocular images and patient characteristics were presented to ChatGPT 4.0, Gemini Advanced 1.5 Pro, and Perplexity Pro. Responses were recorded and compared with the clinical diagnoses and treatment recommendations made by two expert human graders. Diagnostic and treatment agreement, accuracy, sensitivity, and specificity were analyzed. Results: Gemini consistently outperformed ChatGPT and Perplexity across diagnostic and treatment prompts. The highest model performance was observed for prompts requesting treatment recommendations with clinical information, where Gemini achieved the highest accuracy (0.725), followed by Perplexity (0.647) and ChatGPT (0.314). Performance was lowest for prompts requiring strict clinical criteria, with all models showing poor sensitivity. Both human graders outperformed all MLLMs in accuracy and sensitivity on most prompts (P < 0.005). Accuracy did not improve when provided demographic or clinical data, except for Gemini. Conclusions: Human graders outperform current MLLMs, which show only moderate ability to diagnose choroidal nevi or melanoma from imaging. Translational Relevance: This study highlights limitations and potential of MLLMs in aiding diagnosis and treatment of choroidal lesions.

Indexed as

Choroid NeoplasmsMelanomaNevus, PigmentedAdultAgedCross-Sectional StudiesFemaleHumansLarge Language ModelsMaleMiddle AgedRetrospective StudiesTomography, Optical CoherenceVisual Acuity

Identifiers

PMID41085353
PMCPMC12530446

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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.