Evidence map›Paper›PMID 41339423›Full record

ArticleScientific reports2025

Evaluating the clinical utility of multimodal large language models in rare maculopathy.

Melanie D Tran, Evan Walker, Ines D Nagel, Nehal Nailesh Mehta, Jesse Most, Henry A Ferreyra, Lesley A Everett, Paul Yang, Mark E Pennesi, Shyamanga Borooah

Abstract read
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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Melanie D TranRetinal Division, Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, 9415 Campus Point Dr, La Jolla, CA, 92093, USA.
Evan WalkerViterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, San Diego, CA, USA.
Ines D NagelRetinal Division, Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, 9415 Campus Point Dr, La Jolla, CA, 92093, USA.
Nehal Nailesh MehtaRetinal Division, Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, 9415 Campus Point Dr, La Jolla, CA, 92093, USA.
Jesse MostRetinal Division, Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, 9415 Campus Point Dr, La Jolla, CA, 92093, USA.
Henry A FerreyraRetinal Division, Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, 9415 Campus Point Dr, La Jolla, CA, 92093, USA.
Lesley A EverettCasey Eye Institute, Oregon Health and Science University, Portland, OR, USA.
Paul YangCasey Eye Institute, Oregon Health and Science University, Portland, OR, USA.
Mark E PennesiCasey Eye Institute, Oregon Health and Science University, Portland, OR, USA.
Shyamanga BorooahRetinal Division, Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, 9415 Campus Point Dr, La Jolla, CA, 92093, USA. sborooah@health.ucsd.edu.

Funding

Proteomics CoreP30EY010572 · NEI · OREGON HEALTH & SCIENCE UNIVERSITY · PI Kate E Keller · 1995 to 2026
$19.4M
Mentored Vision Clinician-Scientist Program at OHSUK12EY027720 · NEI · OREGON HEALTH & SCIENCE UNIVERSITY · PI David Huang · 2018 to 2026
$2.2M
NEI NIH HHS K12 EY027720NEI NIH HHS P30 EY010572NIH HHS P30 EY010572Oregon Health and Science University Malcolm M. Marquis, MD Endowed Fund for Innovation to Casey Eye Institute
6 · The paper itself

Abstract

This study aimed to assess how multimodal large language models (MLLM) diagnose and differentiate Pentosan Polysulfate (PPS) Maculopathy from other phenotypic mimics. A retrospective review of clinical records and multimodal retinal imaging was conducted with patients from the Shiley Eye Institute and Casey Eye Institute. Four MLLMs (ChatGPT-4o, Claude 3.5 Sonnet, Google Gemini 1.5 Pro, Perplexity Llama 3.1 Sonar/Default) along with human retinal specialists answered prompts based on retinal imaging and demographic data. Performance was evaluated using accuracy, sensitivity and specificity estimates. The study included 126 eyes from 63 patients, with 36 eyes with PPS maculopathy, 50 eyes with Stargardt disease, and 40 eyes with PRPH2-associated multifocal pattern dystrophy. MLLMs showed improved accuracy and sensitivity when answer choices were restricted, with ChatGPT consistently performing best when all imaging modalities were prompted together. The inclusion of demographic data further enhanced performance in prompts with limited answer choices. Human retinal specialist evaluations aligned with MLLM performance trends and also improved with demographic data. While MLLMs show diagnostic potential, further refinement is needed before clinical implementation. These findings highlight the importance of prompt design and demographic data to optimize MLLM performance with retinal imaging modalities.

Indexed as

Macular DegenerationAdultAgedDiagnosis, DifferentialFemaleHumansLarge Language ModelsMaleMiddle AgedMultimodal ImagingRetinaRetrospective StudiesSensitivity and SpecificityStargardt DiseasePattern dystrophyPentosan polysulfate sodiumStargardt

Identifiers

PMID41339423
PMCPMC12764543

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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.