Can Multimodal Large Language Models Diagnose Diabetic Retinopathy from Fundus Photos? A Quantitative Evaluation
To evaluate the diagnostic accuracy of 4 multimodal large language models (MLLMs) in detecting and grading diabetic retinopathy (DR) using their new image analysis features. A single-center retrospective study. Patients diagnosed with prediabetes and diabetes. Ultra-widefield fundus images from pati...
Saved in:
| Published in: | Ophthalmology science (Online) Vol. 6; no. 1; p. 100911 |
|---|---|
| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Netherlands
Elsevier Inc
01.01.2026
Elsevier |
| Subjects: | |
| ISSN: | 2666-9145, 2666-9145 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | To evaluate the diagnostic accuracy of 4 multimodal large language models (MLLMs) in detecting and grading diabetic retinopathy (DR) using their new image analysis features.
A single-center retrospective study.
Patients diagnosed with prediabetes and diabetes.
Ultra-widefield fundus images from patients seen at the University of California, San Diego, were graded for DR severity by 3 retina specialists using the ETDRS classification system to establish ground truth. Four MLLMs (ChatGPT-4o, Claude 3.5 Sonnet, Google Gemini 1.5 Pro, and Perplexity Llama 3.1 Sonar/Default) were tested using 4 distinct prompts. These assessed multiple-choice disease diagnosis, binary disease classification, and disease severity. Multimodal large language models were assessed for accuracy, sensitivity, and specificity in identifying the presence or absence of DR and relative disease severity.
Accuracy, sensitivity, and specificity of diagnosis.
A total of 309 eyes from 188 patients were included in the study. The average patient age was 58.7 (56.7–60.7) years, with 55.3% being female. After specialist grading, 70.2% of eyes had DR of varying severity, and 29.8% had no DR. For disease identification with multiple choices provided, Claude and ChatGPT scored significantly higher (P < 0.0006, per Bonferroni correction) than other MLLMs for accuracy (0.608–0.566) and sensitivity (0.618–0.641). In binary DR versus no DR classification, accuracy was the highest for ChatGPT (0.644) and Perplexity (0.602). Sensitivity varied (ChatGPT [0.539], Perplexity [0.488], Claude [0.179], and Gemini [0.042]), whereas specificity for all models was relatively high (range: 0.870–0.989). For the DR severity prompt with the best overall results (Prompt 3.1), no significant differences between models were found in accuracy (Perplexity [0.411], ChatGPT [0.395], Gemini [0.392], and Claude [0.314]). All models demonstrated low sensitivity (Perplexity [0.247], ChatGPT [0.229], Gemini [0.224], and Claude [0.184]). Specificity ranged from 0.840 to 0.866.
Multimodal large language models are powerful tools that may eventually assist retinal image analysis. Currently, however, there is variability in the accuracy of image analysis, and diagnostic performance falls short of clinical standards for safe implementation in DR diagnosis and grading. Further training and optimization of common errors may enhance their clinical utility.
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. |
|---|---|
| AbstractList | Objective: To evaluate the diagnostic accuracy of 4 multimodal large language models (MLLMs) in detecting and grading diabetic retinopathy (DR) using their new image analysis features. Design: A single-center retrospective study. Subjects: Patients diagnosed with prediabetes and diabetes. Methods: Ultra-widefield fundus images from patients seen at the University of California, San Diego, were graded for DR severity by 3 retina specialists using the ETDRS classification system to establish ground truth. Four MLLMs (ChatGPT-4o, Claude 3.5 Sonnet, Google Gemini 1.5 Pro, and Perplexity Llama 3.1 Sonar/Default) were tested using 4 distinct prompts. These assessed multiple-choice disease diagnosis, binary disease classification, and disease severity. Multimodal large language models were assessed for accuracy, sensitivity, and specificity in identifying the presence or absence of DR and relative disease severity. Main Outcome Measures: Accuracy, sensitivity, and specificity of diagnosis. Results: A total of 309 eyes from 188 patients were included in the study. The average patient age was 58.7 (56.7–60.7) years, with 55.3% being female. After specialist grading, 70.2% of eyes had DR of varying severity, and 29.8% had no DR. For disease identification with multiple choices provided, Claude and ChatGPT scored significantly higher (P < 0.0006, per Bonferroni correction) than other MLLMs for accuracy (0.608–0.566) and sensitivity (0.618–0.641). In binary DR versus no DR classification, accuracy was the highest for ChatGPT (0.644) and Perplexity (0.602). Sensitivity varied (ChatGPT [0.539], Perplexity [0.488], Claude [0.179], and Gemini [0.042]), whereas specificity for all models was relatively high (range: 0.870–0.989). For the DR severity prompt with the best overall results (Prompt 3.1), no significant differences between models were found in accuracy (Perplexity [0.411], ChatGPT [0.395], Gemini [0.392], and Claude [0.314]). All models demonstrated low sensitivity (Perplexity [0.247], ChatGPT [0.229], Gemini [0.224], and Claude [0.184]). Specificity ranged from 0.840 to 0.866. Conclusions: Multimodal large language models are powerful tools that may eventually assist retinal image analysis. Currently, however, there is variability in the accuracy of image analysis, and diagnostic performance falls short of clinical standards for safe implementation in DR diagnosis and grading. Further training and optimization of common errors may enhance their clinical utility. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. To evaluate the diagnostic accuracy of 4 multimodal large language models (MLLMs) in detecting and grading diabetic retinopathy (DR) using their new image analysis features. A single-center retrospective study. Patients diagnosed with prediabetes and diabetes. Ultra-widefield fundus images from patients seen at the University of California, San Diego, were graded for DR severity by 3 retina specialists using the ETDRS classification system to establish ground truth. Four MLLMs (ChatGPT-4o, Claude 3.5 Sonnet, Google Gemini 1.5 Pro, and Perplexity Llama 3.1 Sonar/Default) were tested using 4 distinct prompts. These assessed multiple-choice disease diagnosis, binary disease classification, and disease severity. Multimodal large language models were assessed for accuracy, sensitivity, and specificity in identifying the presence or absence of DR and relative disease severity. Accuracy, sensitivity, and specificity of diagnosis. A total of 309 eyes from 188 patients were included in the study. The average patient age was 58.7 (56.7–60.7) years, with 55.3% being female. After specialist grading, 70.2% of eyes had DR of varying severity, and 29.8% had no DR. For disease identification with multiple choices provided, Claude and ChatGPT scored significantly higher (P < 0.0006, per Bonferroni correction) than other MLLMs for accuracy (0.608–0.566) and sensitivity (0.618–0.641). In binary DR versus no DR classification, accuracy was the highest for ChatGPT (0.644) and Perplexity (0.602). Sensitivity varied (ChatGPT [0.539], Perplexity [0.488], Claude [0.179], and Gemini [0.042]), whereas specificity for all models was relatively high (range: 0.870–0.989). For the DR severity prompt with the best overall results (Prompt 3.1), no significant differences between models were found in accuracy (Perplexity [0.411], ChatGPT [0.395], Gemini [0.392], and Claude [0.314]). All models demonstrated low sensitivity (Perplexity [0.247], ChatGPT [0.229], Gemini [0.224], and Claude [0.184]). Specificity ranged from 0.840 to 0.866. Multimodal large language models are powerful tools that may eventually assist retinal image analysis. Currently, however, there is variability in the accuracy of image analysis, and diagnostic performance falls short of clinical standards for safe implementation in DR diagnosis and grading. Further training and optimization of common errors may enhance their clinical utility. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. To evaluate the diagnostic accuracy of 4 multimodal large language models (MLLMs) in detecting and grading diabetic retinopathy (DR) using their new image analysis features. A single-center retrospective study. Patients diagnosed with prediabetes and diabetes. Ultra-widefield fundus images from patients seen at the University of California, San Diego, were graded for DR severity by 3 retina specialists using the ETDRS classification system to establish ground truth. Four MLLMs (ChatGPT-4o, Claude 3.5 Sonnet, Google Gemini 1.5 Pro, and Perplexity Llama 3.1 Sonar/Default) were tested using 4 distinct prompts. These assessed multiple-choice disease diagnosis, binary disease classification, and disease severity. Multimodal large language models were assessed for accuracy, sensitivity, and specificity in identifying the presence or absence of DR and relative disease severity. Accuracy, sensitivity, and specificity of diagnosis. A total of 309 eyes from 188 patients were included in the study. The average patient age was 58.7 (56.7-60.7) years, with 55.3% being female. After specialist grading, 70.2% of eyes had DR of varying severity, and 29.8% had no DR. For disease identification with multiple choices provided, Claude and ChatGPT scored significantly higher ( < 0.0006, per Bonferroni correction) than other MLLMs for accuracy (0.608-0.566) and sensitivity (0.618-0.641). In binary DR versus no DR classification, accuracy was the highest for ChatGPT (0.644) and Perplexity (0.602). Sensitivity varied (ChatGPT [0.539], Perplexity [0.488], Claude [0.179], and Gemini [0.042]), whereas specificity for all models was relatively high (range: 0.870-0.989). For the DR severity prompt with the best overall results (Prompt 3.1), no significant differences between models were found in accuracy (Perplexity [0.411], ChatGPT [0.395], Gemini [0.392], and Claude [0.314]). All models demonstrated low sensitivity (Perplexity [0.247], ChatGPT [0.229], Gemini [0.224], and Claude [0.184]). Specificity ranged from 0.840 to 0.866. Multimodal large language models are powerful tools that may eventually assist retinal image analysis. Currently, however, there is variability in the accuracy of image analysis, and diagnostic performance falls short of clinical standards for safe implementation in DR diagnosis and grading. Further training and optimization of common errors may enhance their clinical utility. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. To evaluate the diagnostic accuracy of 4 multimodal large language models (MLLMs) in detecting and grading diabetic retinopathy (DR) using their new image analysis features.ObjectiveTo evaluate the diagnostic accuracy of 4 multimodal large language models (MLLMs) in detecting and grading diabetic retinopathy (DR) using their new image analysis features.A single-center retrospective study.DesignA single-center retrospective study.Patients diagnosed with prediabetes and diabetes.SubjectsPatients diagnosed with prediabetes and diabetes.Ultra-widefield fundus images from patients seen at the University of California, San Diego, were graded for DR severity by 3 retina specialists using the ETDRS classification system to establish ground truth. Four MLLMs (ChatGPT-4o, Claude 3.5 Sonnet, Google Gemini 1.5 Pro, and Perplexity Llama 3.1 Sonar/Default) were tested using 4 distinct prompts. These assessed multiple-choice disease diagnosis, binary disease classification, and disease severity. Multimodal large language models were assessed for accuracy, sensitivity, and specificity in identifying the presence or absence of DR and relative disease severity.MethodsUltra-widefield fundus images from patients seen at the University of California, San Diego, were graded for DR severity by 3 retina specialists using the ETDRS classification system to establish ground truth. Four MLLMs (ChatGPT-4o, Claude 3.5 Sonnet, Google Gemini 1.5 Pro, and Perplexity Llama 3.1 Sonar/Default) were tested using 4 distinct prompts. These assessed multiple-choice disease diagnosis, binary disease classification, and disease severity. Multimodal large language models were assessed for accuracy, sensitivity, and specificity in identifying the presence or absence of DR and relative disease severity.Accuracy, sensitivity, and specificity of diagnosis.Main Outcome MeasuresAccuracy, sensitivity, and specificity of diagnosis.A total of 309 eyes from 188 patients were included in the study. The average patient age was 58.7 (56.7-60.7) years, with 55.3% being female. After specialist grading, 70.2% of eyes had DR of varying severity, and 29.8% had no DR. For disease identification with multiple choices provided, Claude and ChatGPT scored significantly higher (P < 0.0006, per Bonferroni correction) than other MLLMs for accuracy (0.608-0.566) and sensitivity (0.618-0.641). In binary DR versus no DR classification, accuracy was the highest for ChatGPT (0.644) and Perplexity (0.602). Sensitivity varied (ChatGPT [0.539], Perplexity [0.488], Claude [0.179], and Gemini [0.042]), whereas specificity for all models was relatively high (range: 0.870-0.989). For the DR severity prompt with the best overall results (Prompt 3.1), no significant differences between models were found in accuracy (Perplexity [0.411], ChatGPT [0.395], Gemini [0.392], and Claude [0.314]). All models demonstrated low sensitivity (Perplexity [0.247], ChatGPT [0.229], Gemini [0.224], and Claude [0.184]). Specificity ranged from 0.840 to 0.866.ResultsA total of 309 eyes from 188 patients were included in the study. The average patient age was 58.7 (56.7-60.7) years, with 55.3% being female. After specialist grading, 70.2% of eyes had DR of varying severity, and 29.8% had no DR. For disease identification with multiple choices provided, Claude and ChatGPT scored significantly higher (P < 0.0006, per Bonferroni correction) than other MLLMs for accuracy (0.608-0.566) and sensitivity (0.618-0.641). In binary DR versus no DR classification, accuracy was the highest for ChatGPT (0.644) and Perplexity (0.602). Sensitivity varied (ChatGPT [0.539], Perplexity [0.488], Claude [0.179], and Gemini [0.042]), whereas specificity for all models was relatively high (range: 0.870-0.989). For the DR severity prompt with the best overall results (Prompt 3.1), no significant differences between models were found in accuracy (Perplexity [0.411], ChatGPT [0.395], Gemini [0.392], and Claude [0.314]). All models demonstrated low sensitivity (Perplexity [0.247], ChatGPT [0.229], Gemini [0.224], and Claude [0.184]). Specificity ranged from 0.840 to 0.866.Multimodal large language models are powerful tools that may eventually assist retinal image analysis. Currently, however, there is variability in the accuracy of image analysis, and diagnostic performance falls short of clinical standards for safe implementation in DR diagnosis and grading. Further training and optimization of common errors may enhance their clinical utility.ConclusionsMultimodal large language models are powerful tools that may eventually assist retinal image analysis. Currently, however, there is variability in the accuracy of image analysis, and diagnostic performance falls short of clinical standards for safe implementation in DR diagnosis and grading. Further training and optimization of common errors may enhance their clinical utility.Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.Financial DisclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. ObjectiveTo evaluate the diagnostic accuracy of 4 multimodal large language models (MLLMs) in detecting and grading diabetic retinopathy (DR) using their new image analysis features. DesignA single-center retrospective study. SubjectsPatients diagnosed with prediabetes and diabetes. MethodsUltra-widefield fundus images from patients seen at the University of California, San Diego, were graded for DR severity by 3 retina specialists using the ETDRS classification system to establish ground truth. Four MLLMs (ChatGPT-4o, Claude 3.5 Sonnet, Google Gemini 1.5 Pro, and Perplexity Llama 3.1 Sonar/Default) were tested using 4 distinct prompts. These assessed multiple-choice disease diagnosis, binary disease classification, and disease severity. Multimodal large language models were assessed for accuracy, sensitivity, and specificity in identifying the presence or absence of DR and relative disease severity. Main Outcome MeasuresAccuracy, sensitivity, and specificity of diagnosis. ResultsA total of 309 eyes from 188 patients were included in the study. The average patient age was 58.7 (56.7–60.7) years, with 55.3% being female. After specialist grading, 70.2% of eyes had DR of varying severity, and 29.8% had no DR. For disease identification with multiple choices provided, Claude and ChatGPT scored significantly higher ( P < 0.0006, per Bonferroni correction) than other MLLMs for accuracy (0.608–0.566) and sensitivity (0.618–0.641). In binary DR versus no DR classification, accuracy was the highest for ChatGPT (0.644) and Perplexity (0.602). Sensitivity varied (ChatGPT [0.539], Perplexity [0.488], Claude [0.179], and Gemini [0.042]), whereas specificity for all models was relatively high (range: 0.870–0.989). For the DR severity prompt with the best overall results (Prompt 3.1), no significant differences between models were found in accuracy (Perplexity [0.411], ChatGPT [0.395], Gemini [0.392], and Claude [0.314]). All models demonstrated low sensitivity (Perplexity [0.247], ChatGPT [0.229], Gemini [0.224], and Claude [0.184]). Specificity ranged from 0.840 to 0.866. ConclusionsMultimodal large language models are powerful tools that may eventually assist retinal image analysis. Currently, however, there is variability in the accuracy of image analysis, and diagnostic performance falls short of clinical standards for safe implementation in DR diagnosis and grading. Further training and optimization of common errors may enhance their clinical utility. Financial Disclosure(s)Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. |
| ArticleNumber | 100911 |
| Author | Scott, Nathan L. Walker, Evan H. Borooah, Shyamanga Most, Jesse A. Chen, Jimmy S. Nagel, Ines D. Mehta, Nehal N. Russell, Jonathan F. |
| Author_xml | – sequence: 1 givenname: Jesse A. orcidid: 0000-0002-2582-4503 surname: Most fullname: Most, Jesse A. organization: Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, California – sequence: 2 givenname: Evan H. surname: Walker fullname: Walker, Evan H. organization: Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California – sequence: 3 givenname: Nehal N. surname: Mehta fullname: Mehta, Nehal N. organization: Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, California – sequence: 4 givenname: Ines D. surname: Nagel fullname: Nagel, Ines D. organization: Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, California – sequence: 5 givenname: Jimmy S. surname: Chen fullname: Chen, Jimmy S. organization: Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California – sequence: 6 givenname: Jonathan F. surname: Russell fullname: Russell, Jonathan F. organization: Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, Iowa – sequence: 7 givenname: Nathan L. surname: Scott fullname: Scott, Nathan L. organization: Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, California – sequence: 8 givenname: Shyamanga surname: Borooah fullname: Borooah, Shyamanga email: sborooah@health.ucsd.edu organization: Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, California |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/41030829$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFUk1v1DAQjVAR_aB_gAPKkcsuthN7Y4SoqqWFSlvxLXGzJs546yVrBztZdf89DikVQgIu46fRe280fnOcHTjvMMueUDKnhIrnm_mt7-KcEcZTg0hKH2RHTAgxk7TkB7_hw-w0xg0hiUkLVtJH2WFJSUEqJo-ybgkuvx7a3m59A22-grDGVN16gASufYNtzF9bWDsfcQQ19lbnH1N1voP-Zp-b4Lf55eCaIebvb3zv41l-nn8YwPW2h97uML_YQTsk6N3j7KGBNuLp3XuSfbm8-Lx8O1u9e3O1PF_NNGe8n2kooCgaWYCkErkpRWNqU1YVEt3QQksjSkm0FEZiuRgRmNIA8NTQgtbFSXY1-TYeNqoLdgthrzxY9bPhw1pBSJu0qDiQBaKpKmKg5LyukIlKEE0XpgRdmOT1bPLqgv8-YOzV1kaNbQsO_RBVwbigYwgkUZ_eUYd6i8394F8_nghsIujgYwxo7imUqDFZtVFjsmpMVk3JJtHLSZTCwJ3FoKK26DQ2NqDu00r23_JXf8h1a53V0H7DPcaNH4JLWSiqIlNEfRpPZ7wcxtPREPk1Gbz4u8H_pv8A4T7Ulg |
| Cites_doi | 10.2337/dci23-0032 10.1177/1932296820967011 10.1159/000486284 10.2196/50638 10.1016/j.ajo.2024.02.012 10.1016/j.ophtha.2016.11.014 10.5888/pcd13.160193 10.4103/tjo.TJO-D-23-00193 10.4239/wjd.v4.i6.290 10.1111/nan.12997 10.1016/j.ophtha.2018.01.034 10.1038/s41574-020-00451-4 10.1186/s40942-024-00595-9 10.1038/s41433-024-03074-5 10.1016/j.xops.2024.100667 10.1016/j.ophtha.2013.05.004 10.1136/bjo-2023-324533 10.4172/2155-9570.1000582 10.1016/j.ophtha.2018.04.007 10.1016/j.diabres.2021.109119 10.1016/S2589-7500(20)30060-1 10.1016/j.ajo.2012.03.019 10.1016/j.ophtha.2019.09.025 10.1136/bjo-2024-325459 10.1097/APO.0000000000000403 10.2196/45312 10.1016/j.ajoint.2025.100111 10.1002/hsr2.2004 10.1016/j.xops.2024.100495 10.1136/bjo-2023-325054 10.3389/fmed.2025.1519768 10.1186/s40942-025-00670-9 10.1016/j.eclinm.2023.102387 10.1186/s40942-023-00511-7 10.2147/OPTH.S435052 10.1016/j.xops.2024.100600 10.1371/journal.pone.0198979 10.1016/j.compbiomed.2024.108709 |
| ContentType | Journal Article |
| Copyright | 2025 American Academy of Ophthalmology American Academy of Ophthalmology 2025 by the American Academy of Ophthalmologyé. |
| Copyright_xml | – notice: 2025 American Academy of Ophthalmology – notice: American Academy of Ophthalmology – notice: 2025 by the American Academy of Ophthalmologyé. |
| DBID | 6I. AAFTH AAYXX CITATION NPM 7X8 DOA |
| DOI | 10.1016/j.xops.2025.100911 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef PubMed MEDLINE - Academic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2666-9145 |
| EndPage | 100911 |
| ExternalDocumentID | oai_doaj_org_article_5a07eef880fa455b8e26860c17f4ac3f 41030829 10_1016_j_xops_2025_100911 S266691452500209X 1_s2_0_S266691452500209X |
| Genre | Journal Article |
| GroupedDBID | .1- .FO 0R~ AAEDW AALRI AAXUO AAYWO ACVFH ADCNI ADVLN AEUPX AFJKZ AFPUW AFRHN AIGII AITUG AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ APXCP EBS FDB GROUPED_DOAJ M~E OK1 ROL RPM Z5R 6I. AAFTH AAYXX CITATION NPM 7X8 |
| ID | FETCH-LOGICAL-c525t-ca3a33d93a919e5f46dfbf488e0cd13c9f6490c96f9e4790c9af4faa56f9c61b3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001576792300002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2666-9145 |
| IngestDate | Fri Oct 03 12:19:12 EDT 2025 Mon Oct 06 16:26:25 EDT 2025 Sat Oct 25 11:10:42 EDT 2025 Thu Nov 20 00:45:13 EST 2025 Sat Oct 25 17:13:26 EDT 2025 Sun Oct 19 01:20:41 EDT 2025 Sat Oct 25 11:12:41 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Diabetic retinopathy MLLM UWF AI LLM DR Image analysis NPDR MC Ultra-widefield fundus photography Multimodal large language model Artificial intelligence PDR ultra-widefield nonproliferative diabetic retinopathy large language model multiple-choice proliferative diabetic retinopathy |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. 2025 by the American Academy of Ophthalmologyé. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c525t-ca3a33d93a919e5f46dfbf488e0cd13c9f6490c96f9e4790c9af4faa56f9c61b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-2582-4503 |
| OpenAccessLink | https://doaj.org/article/5a07eef880fa455b8e26860c17f4ac3f |
| PMID | 41030829 |
| PQID | 3256110090 |
| PQPubID | 23479 |
| PageCount | 1 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_5a07eef880fa455b8e26860c17f4ac3f proquest_miscellaneous_3256110090 pubmed_primary_41030829 crossref_primary_10_1016_j_xops_2025_100911 elsevier_sciencedirect_doi_10_1016_j_xops_2025_100911 elsevier_clinicalkeyesjournals_1_s2_0_S266691452500209X elsevier_clinicalkey_doi_10_1016_j_xops_2025_100911 |
| PublicationCentury | 2000 |
| PublicationDate | 2026-01-01 |
| PublicationDateYYYYMMDD | 2026-01-01 |
| PublicationDate_xml | – month: 01 year: 2026 text: 2026-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Netherlands |
| PublicationPlace_xml | – name: Netherlands |
| PublicationTitle | Ophthalmology science (Online) |
| PublicationTitleAlternate | Ophthalmol Sci |
| PublicationYear | 2026 |
| Publisher | Elsevier Inc Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier |
| References | Flaxel, Adelman, Bailey (bib25) 2020; 127 Sun, Saeedi, Karuranga (bib40) 2022; 183 Chia, Antaki, Zhou (bib15) 2024; 108 Mohammadi, Nguyen (bib9) 2024; 4 Agnihotri, Nagel, Artiaga (bib11) 2024; 5 Ruamviboonsuk, Chantra, Seresirikachorn (bib35) 2021; 10 Guo, Wei, Sun (bib17) 2024; 178 Raghu, S, S Devishamani (bib43) 2023; 17 Chen, Reddy, Al-Sharif (bib12) 2025; 5 Gilson, Safranek, Huang (bib8) 2023; 9 Xu, Chen, Zhao, Shi (bib20) 2024; 108 Hossain, Al-Mamun, Islam (bib1) 2024; 7 Antonetti, Silva, Stitt (bib2) 2021; 17 Wu, Nishida, Moghimi, Weinreb (bib22) 2024; 14 Cheong, Zhang, Tan (bib41) 2024; 108 Hansen, Tang (bib33) 2016; 7 Rajesh, Davidson, Lee, Lee (bib30) 2023; 46 Meskó (bib10) 2023; 25 Gupta, Al-Kazwini (bib44) 2024; 16 Tahir, Ullah, Tahir (bib28) 2025; 12 Mihalache, Huang, Cruz-Pimentel (bib16) 2024; 38 Joseph, Selvaraj, Mani (bib7) 2024; 263 Hu, Joseph, Li (bib29) 2024; 67 Nørgaard, Grauslund (bib4) 2018; 60 Ono, Dickson, Koga (bib32) 2024; 50 Wu, Fernandez-Loaiza, Sauma (bib26) 2013; 4 Krause, Gulshan, Rahimy (bib31) 2018; 125 Tufail, Rudisill, Egan (bib38) 2017; 124 Alqahtani, Alshareef, Aljadani (bib27) 2025; 11 Wong, Sun, Kawasaki (bib3) 2018; 125 Rizvi, Rizvi, Lalakia (bib34) 2023; 15 Lu (bib5) 2016; 13 bib18 Ferro, Roth, Zinkernagel, Anguita (bib42) 2023; 9 Piyasena, Murthy, Yip (bib6) 2019; 14 Xie, Nguyen, Hamzah (bib37) 2020; 2 bib39 Bellanda, Santos, Ferraz (bib13) 2024; 10 Silva, Cavallerano, Sun (bib23) 2012; 154 Fuller, Hu, Liu (bib36) 2020; 16 Aftab, Khan, VanderBeek (bib21) 2025; 2 Silva, Cavallerano, Sun (bib24) 2013; 120 Jalili, Jiravarnsirikul, Bowd (bib14) 2025; 5 Piyasena (10.1016/j.xops.2025.100911_bib6) 2019; 14 Gilson (10.1016/j.xops.2025.100911_bib8) 2023; 9 Guo (10.1016/j.xops.2025.100911_bib17) 2024; 178 Alqahtani (10.1016/j.xops.2025.100911_bib27) 2025; 11 Jalili (10.1016/j.xops.2025.100911_bib14) 2025; 5 Chen (10.1016/j.xops.2025.100911_bib12) 2025; 5 Tahir (10.1016/j.xops.2025.100911_bib28) 2025; 12 Krause (10.1016/j.xops.2025.100911_bib31) 2018; 125 Cheong (10.1016/j.xops.2025.100911_bib41) 2024; 108 Nørgaard (10.1016/j.xops.2025.100911_bib4) 2018; 60 Bellanda (10.1016/j.xops.2025.100911_bib13) 2024; 10 Ferro (10.1016/j.xops.2025.100911_bib42) 2023; 9 Raghu (10.1016/j.xops.2025.100911_bib43) 2023; 17 Wu (10.1016/j.xops.2025.100911_bib26) 2013; 4 Gupta (10.1016/j.xops.2025.100911_bib44) 2024; 16 Lu (10.1016/j.xops.2025.100911_bib5) 2016; 13 Rajesh (10.1016/j.xops.2025.100911_bib30) 2023; 46 Silva (10.1016/j.xops.2025.100911_bib24) 2013; 120 Flaxel (10.1016/j.xops.2025.100911_bib25) 2020; 127 Fuller (10.1016/j.xops.2025.100911_bib36) 2020; 16 Mihalache (10.1016/j.xops.2025.100911_bib16) 2024; 38 Xu (10.1016/j.xops.2025.100911_bib20) 2024; 108 Wu (10.1016/j.xops.2025.100911_bib22) 2024; 14 Ruamviboonsuk (10.1016/j.xops.2025.100911_bib35) 2021; 10 Hossain (10.1016/j.xops.2025.100911_bib1) 2024; 7 Mohammadi (10.1016/j.xops.2025.100911_bib9) 2024; 4 Meskó (10.1016/j.xops.2025.100911_bib10) 2023; 25 Ono (10.1016/j.xops.2025.100911_bib32) 2024; 50 Silva (10.1016/j.xops.2025.100911_bib23) 2012; 154 Chia (10.1016/j.xops.2025.100911_bib15) 2024; 108 Aftab (10.1016/j.xops.2025.100911_bib21) 2025; 2 Sun (10.1016/j.xops.2025.100911_bib40) 2022; 183 Rizvi (10.1016/j.xops.2025.100911_bib34) 2023; 15 Agnihotri (10.1016/j.xops.2025.100911_bib11) 2024; 5 Hansen (10.1016/j.xops.2025.100911_bib33) 2016; 7 Tufail (10.1016/j.xops.2025.100911_bib38) 2017; 124 Joseph (10.1016/j.xops.2025.100911_bib7) 2024; 263 Xie (10.1016/j.xops.2025.100911_bib37) 2020; 2 Antonetti (10.1016/j.xops.2025.100911_bib2) 2021; 17 Hu (10.1016/j.xops.2025.100911_bib29) 2024; 67 Wong (10.1016/j.xops.2025.100911_bib3) 2018; 125 |
| References_xml | – volume: 5 year: 2025 ident: bib14 article-title: Glaucoma detection and feature identification via GPT-4V fundus image analysis publication-title: Ophthalmol Sci – volume: 4 start-page: 290 year: 2013 end-page: 294 ident: bib26 article-title: Classification of diabetic retinopathy and diabetic macular edema publication-title: World J Diabetes – volume: 9 year: 2023 ident: bib8 article-title: How does ChatGPT perform on the united States Medical Licensing Examination (USMLE)? The implications of large Language models for medical education and knowledge assessment publication-title: JMIR Med Educ – volume: 17 start-page: 4021 year: 2023 end-page: 4031 ident: bib43 article-title: The utility of ChatGPT in diabetic retinopathy risk assessment: a comparative study with clinical diagnosis publication-title: Clin Ophthalmol Auckl NZ – volume: 120 start-page: 2587 year: 2013 end-page: 2595 ident: bib24 article-title: Peripheral lesions identified by mydriatic ultrawide field imaging: distribution and potential impact on diabetic retinopathy severity publication-title: Ophthalmology – volume: 46 start-page: 1728 year: 2023 end-page: 1739 ident: bib30 article-title: Artificial intelligence and diabetic retinopathy: AI framework, prospective studies, head-to-head validation, and cost-effectiveness publication-title: Diabetes Care – volume: 16 start-page: 415 year: 2020 end-page: 427 ident: bib36 article-title: Five-year cost-effectiveness modeling of primary care-based, nonmydriatic automated retinal image analysis screening among low-income patients with diabetes publication-title: J Diabetes Sci Technol – ident: bib39 article-title: ChatGPT pricing – volume: 127 start-page: P66 year: 2020 end-page: P145 ident: bib25 article-title: Diabetic retinopathy preferred practice pattern publication-title: Ophthalmology – volume: 7 start-page: 4 year: 2016 ident: bib33 article-title: Automated detection of diabetic retinopathy in three European populations - university of surrey publication-title: J Clin Exp Ophthalmol – volume: 108 year: 2024 ident: bib15 article-title: Foundation models in ophthalmology publication-title: Br J Ophthalmol – volume: 125 start-page: 1264 year: 2018 end-page: 1272 ident: bib31 article-title: Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy publication-title: Ophthalmology – volume: 7 year: 2024 ident: bib1 article-title: Diabetes mellitus, the fastest growing global public health concern: early detection should be focused publication-title: Health Sci Rep – ident: bib18 article-title: GPT-4V(ision) system card – volume: 5 year: 2025 ident: bib12 article-title: Analysis of ChatGPT responses to ophthalmic cases: can ChatGPT think like an ophthalmologist? publication-title: Ophthalmol Sci – volume: 125 start-page: 1608 year: 2018 end-page: 1622 ident: bib3 article-title: Guidelines on diabetic eye care: the international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings publication-title: Ophthalmology – volume: 10 start-page: 307 year: 2021 end-page: 316 ident: bib35 article-title: Economic evaluations of artificial intelligence in ophthalmology publication-title: Asia Pac J Ophthalmol – volume: 38 start-page: 2491 year: 2024 end-page: 2493 ident: bib16 article-title: Artificial intelligence chatbot interpretation of ophthalmic multimodal imaging cases publication-title: Eye – volume: 67 year: 2024 ident: bib29 article-title: Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: a cost effectiveness analysis publication-title: EClinicalMedicine – volume: 263 start-page: 214 year: 2024 end-page: 230 ident: bib7 article-title: Diagnostic accuracy of artificial intelligence-based automated diabetic retinopathy screening in real-world settings: a systematic review and meta-analysis publication-title: Am J Ophthalmol – volume: 124 start-page: 343 year: 2017 end-page: 351 ident: bib38 article-title: Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders publication-title: Ophthalmology – volume: 108 start-page: 1384 year: 2024 end-page: 1389 ident: bib20 article-title: Unveiling the clinical incapabilities: a benchmarking study of GPT-4V(ision) for ophthalmic multimodal image analysis publication-title: Br J Ophthalmol – volume: 10 start-page: 79 year: 2024 ident: bib13 article-title: Applications of ChatGPT in the diagnosis, management, education, and research of retinal diseases: a scoping review publication-title: Int J Retina Vitr – volume: 11 start-page: 48 year: 2025 ident: bib27 article-title: The efficacy of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis publication-title: Int J Retina Vitr – volume: 108 start-page: 1443 year: 2024 end-page: 1449 ident: bib41 article-title: Comparing generative and retrieval-based chatbots in answering patient questions regarding age-related macular degeneration and diabetic retinopathy publication-title: Br J Ophthalmol – volume: 5 year: 2024 ident: bib11 article-title: Large language models in ophthalmology: a review of publications from top ophthalmology journals publication-title: Ophthalmol Sci – volume: 60 start-page: 9 year: 2018 end-page: 17 ident: bib4 article-title: Automated screening for diabetic retinopathy – a systematic review publication-title: Ophthalmic Res – volume: 14 year: 2019 ident: bib6 article-title: Systematic review on barriers and enablers for access to diabetic retinopathy screening services in different income settings publication-title: PLoS One – volume: 13 start-page: E140 year: 2016 ident: bib5 article-title: Divergent perceptions of barriers to diabetic retinopathy screening among patients and care providers, Los Angeles, California, 2014–2015 publication-title: Prev Chronic Dis – volume: 2 start-page: e240 year: 2020 end-page: e249 ident: bib37 article-title: Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study publication-title: Lancet Digit Health – volume: 25 year: 2023 ident: bib10 article-title: Prompt engineering as an important emerging skill for medical professionals: tutorial publication-title: J Med Internet Res – volume: 17 start-page: 195 year: 2021 end-page: 206 ident: bib2 article-title: Current understanding of the molecular and cellular pathology of diabetic retinopathy publication-title: Nat Rev Endocrinol – volume: 12 year: 2025 ident: bib28 article-title: Artificial intelligence versus manual screening for the detection of diabetic retinopathy: a comparative systematic review and meta-analysis publication-title: Front Med – volume: 15 year: 2023 ident: bib34 article-title: Is artificial intelligence the cost-saving lens to diabetic retinopathy screening in Low- and middle-income countries? publication-title: Cureus – volume: 14 start-page: 454 year: 2024 end-page: 457 ident: bib22 article-title: Effects of prompt engineering on large language model performance in response to questions on common ophthalmic conditions publication-title: Taiwan J Ophthalmol – volume: 9 start-page: 71 year: 2023 ident: bib42 article-title: Application and accuracy of artificial intelligence-derived large language models in patients with age related macular degeneration publication-title: Int J Retina Vitr – volume: 16 year: 2024 ident: bib44 article-title: Evaluating chatgpt's diagnostic accuracy in detecting fundus images publication-title: Cureus – volume: 2 year: 2025 ident: bib21 article-title: Evaluation of ChatGPT-4 in detecting referable diabetic retinopathy using single fundus images publication-title: AJO Int – volume: 50 year: 2024 ident: bib32 article-title: Evaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: a comparative analysis with convolutional neural network model publication-title: Neuropathol Appl Neurobiol – volume: 183 year: 2022 ident: bib40 article-title: IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045 publication-title: Diabetes Res Clin Pract – volume: 178 year: 2024 ident: bib17 article-title: A survey on advancements in image–text multimodal models: from general techniques to biomedical implementations publication-title: Comput Biol Med – volume: 154 start-page: 549 year: 2012 end-page: 559.e2 ident: bib23 article-title: Nonmydriatic ultrawide field retinal imaging compared with dilated standard 7-Field 35-mm photography and retinal specialist examination for evaluation of diabetic retinopathy publication-title: Am J Ophthalmol – volume: 4 year: 2024 ident: bib9 article-title: A user-friendly approach for the diagnosis of diabetic retinopathy using ChatGPT and automated machine learning publication-title: Ophthalmol Sci – volume: 46 start-page: 1728 year: 2023 ident: 10.1016/j.xops.2025.100911_bib30 article-title: Artificial intelligence and diabetic retinopathy: AI framework, prospective studies, head-to-head validation, and cost-effectiveness publication-title: Diabetes Care doi: 10.2337/dci23-0032 – volume: 16 start-page: 415 year: 2020 ident: 10.1016/j.xops.2025.100911_bib36 article-title: Five-year cost-effectiveness modeling of primary care-based, nonmydriatic automated retinal image analysis screening among low-income patients with diabetes publication-title: J Diabetes Sci Technol doi: 10.1177/1932296820967011 – volume: 60 start-page: 9 year: 2018 ident: 10.1016/j.xops.2025.100911_bib4 article-title: Automated screening for diabetic retinopathy – a systematic review publication-title: Ophthalmic Res doi: 10.1159/000486284 – volume: 25 year: 2023 ident: 10.1016/j.xops.2025.100911_bib10 article-title: Prompt engineering as an important emerging skill for medical professionals: tutorial publication-title: J Med Internet Res doi: 10.2196/50638 – volume: 263 start-page: 214 year: 2024 ident: 10.1016/j.xops.2025.100911_bib7 article-title: Diagnostic accuracy of artificial intelligence-based automated diabetic retinopathy screening in real-world settings: a systematic review and meta-analysis publication-title: Am J Ophthalmol doi: 10.1016/j.ajo.2024.02.012 – volume: 124 start-page: 343 year: 2017 ident: 10.1016/j.xops.2025.100911_bib38 article-title: Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders publication-title: Ophthalmology doi: 10.1016/j.ophtha.2016.11.014 – volume: 13 start-page: E140 year: 2016 ident: 10.1016/j.xops.2025.100911_bib5 article-title: Divergent perceptions of barriers to diabetic retinopathy screening among patients and care providers, Los Angeles, California, 2014–2015 publication-title: Prev Chronic Dis doi: 10.5888/pcd13.160193 – volume: 14 start-page: 454 year: 2024 ident: 10.1016/j.xops.2025.100911_bib22 article-title: Effects of prompt engineering on large language model performance in response to questions on common ophthalmic conditions publication-title: Taiwan J Ophthalmol doi: 10.4103/tjo.TJO-D-23-00193 – volume: 4 start-page: 290 year: 2013 ident: 10.1016/j.xops.2025.100911_bib26 article-title: Classification of diabetic retinopathy and diabetic macular edema publication-title: World J Diabetes doi: 10.4239/wjd.v4.i6.290 – volume: 50 year: 2024 ident: 10.1016/j.xops.2025.100911_bib32 article-title: Evaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: a comparative analysis with convolutional neural network model publication-title: Neuropathol Appl Neurobiol doi: 10.1111/nan.12997 – volume: 125 start-page: 1264 year: 2018 ident: 10.1016/j.xops.2025.100911_bib31 article-title: Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy publication-title: Ophthalmology doi: 10.1016/j.ophtha.2018.01.034 – volume: 5 year: 2024 ident: 10.1016/j.xops.2025.100911_bib11 article-title: Large language models in ophthalmology: a review of publications from top ophthalmology journals publication-title: Ophthalmol Sci – volume: 17 start-page: 195 year: 2021 ident: 10.1016/j.xops.2025.100911_bib2 article-title: Current understanding of the molecular and cellular pathology of diabetic retinopathy publication-title: Nat Rev Endocrinol doi: 10.1038/s41574-020-00451-4 – volume: 10 start-page: 79 year: 2024 ident: 10.1016/j.xops.2025.100911_bib13 article-title: Applications of ChatGPT in the diagnosis, management, education, and research of retinal diseases: a scoping review publication-title: Int J Retina Vitr doi: 10.1186/s40942-024-00595-9 – volume: 38 start-page: 2491 year: 2024 ident: 10.1016/j.xops.2025.100911_bib16 article-title: Artificial intelligence chatbot interpretation of ophthalmic multimodal imaging cases publication-title: Eye doi: 10.1038/s41433-024-03074-5 – volume: 15 year: 2023 ident: 10.1016/j.xops.2025.100911_bib34 article-title: Is artificial intelligence the cost-saving lens to diabetic retinopathy screening in Low- and middle-income countries? publication-title: Cureus – volume: 5 year: 2025 ident: 10.1016/j.xops.2025.100911_bib14 article-title: Glaucoma detection and feature identification via GPT-4V fundus image analysis publication-title: Ophthalmol Sci doi: 10.1016/j.xops.2024.100667 – volume: 120 start-page: 2587 year: 2013 ident: 10.1016/j.xops.2025.100911_bib24 article-title: Peripheral lesions identified by mydriatic ultrawide field imaging: distribution and potential impact on diabetic retinopathy severity publication-title: Ophthalmology doi: 10.1016/j.ophtha.2013.05.004 – volume: 108 start-page: 1443 year: 2024 ident: 10.1016/j.xops.2025.100911_bib41 article-title: Comparing generative and retrieval-based chatbots in answering patient questions regarding age-related macular degeneration and diabetic retinopathy publication-title: Br J Ophthalmol doi: 10.1136/bjo-2023-324533 – volume: 7 start-page: 4 year: 2016 ident: 10.1016/j.xops.2025.100911_bib33 article-title: Automated detection of diabetic retinopathy in three European populations - university of surrey publication-title: J Clin Exp Ophthalmol doi: 10.4172/2155-9570.1000582 – volume: 125 start-page: 1608 year: 2018 ident: 10.1016/j.xops.2025.100911_bib3 article-title: Guidelines on diabetic eye care: the international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings publication-title: Ophthalmology doi: 10.1016/j.ophtha.2018.04.007 – volume: 183 year: 2022 ident: 10.1016/j.xops.2025.100911_bib40 article-title: IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045 publication-title: Diabetes Res Clin Pract doi: 10.1016/j.diabres.2021.109119 – volume: 16 year: 2024 ident: 10.1016/j.xops.2025.100911_bib44 article-title: Evaluating chatgpt's diagnostic accuracy in detecting fundus images publication-title: Cureus – volume: 2 start-page: e240 year: 2020 ident: 10.1016/j.xops.2025.100911_bib37 article-title: Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study publication-title: Lancet Digit Health doi: 10.1016/S2589-7500(20)30060-1 – volume: 154 start-page: 549 year: 2012 ident: 10.1016/j.xops.2025.100911_bib23 article-title: Nonmydriatic ultrawide field retinal imaging compared with dilated standard 7-Field 35-mm photography and retinal specialist examination for evaluation of diabetic retinopathy publication-title: Am J Ophthalmol doi: 10.1016/j.ajo.2012.03.019 – volume: 127 start-page: P66 year: 2020 ident: 10.1016/j.xops.2025.100911_bib25 article-title: Diabetic retinopathy preferred practice pattern publication-title: Ophthalmology doi: 10.1016/j.ophtha.2019.09.025 – volume: 108 year: 2024 ident: 10.1016/j.xops.2025.100911_bib15 article-title: Foundation models in ophthalmology publication-title: Br J Ophthalmol doi: 10.1136/bjo-2024-325459 – volume: 10 start-page: 307 year: 2021 ident: 10.1016/j.xops.2025.100911_bib35 article-title: Economic evaluations of artificial intelligence in ophthalmology publication-title: Asia Pac J Ophthalmol doi: 10.1097/APO.0000000000000403 – volume: 9 year: 2023 ident: 10.1016/j.xops.2025.100911_bib8 article-title: How does ChatGPT perform on the united States Medical Licensing Examination (USMLE)? The implications of large Language models for medical education and knowledge assessment publication-title: JMIR Med Educ doi: 10.2196/45312 – volume: 2 year: 2025 ident: 10.1016/j.xops.2025.100911_bib21 article-title: Evaluation of ChatGPT-4 in detecting referable diabetic retinopathy using single fundus images publication-title: AJO Int doi: 10.1016/j.ajoint.2025.100111 – volume: 7 year: 2024 ident: 10.1016/j.xops.2025.100911_bib1 article-title: Diabetes mellitus, the fastest growing global public health concern: early detection should be focused publication-title: Health Sci Rep doi: 10.1002/hsr2.2004 – volume: 4 year: 2024 ident: 10.1016/j.xops.2025.100911_bib9 article-title: A user-friendly approach for the diagnosis of diabetic retinopathy using ChatGPT and automated machine learning publication-title: Ophthalmol Sci doi: 10.1016/j.xops.2024.100495 – volume: 108 start-page: 1384 year: 2024 ident: 10.1016/j.xops.2025.100911_bib20 article-title: Unveiling the clinical incapabilities: a benchmarking study of GPT-4V(ision) for ophthalmic multimodal image analysis publication-title: Br J Ophthalmol doi: 10.1136/bjo-2023-325054 – volume: 12 year: 2025 ident: 10.1016/j.xops.2025.100911_bib28 article-title: Artificial intelligence versus manual screening for the detection of diabetic retinopathy: a comparative systematic review and meta-analysis publication-title: Front Med doi: 10.3389/fmed.2025.1519768 – volume: 11 start-page: 48 year: 2025 ident: 10.1016/j.xops.2025.100911_bib27 article-title: The efficacy of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis publication-title: Int J Retina Vitr doi: 10.1186/s40942-025-00670-9 – volume: 67 year: 2024 ident: 10.1016/j.xops.2025.100911_bib29 article-title: Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: a cost effectiveness analysis publication-title: EClinicalMedicine doi: 10.1016/j.eclinm.2023.102387 – volume: 9 start-page: 71 year: 2023 ident: 10.1016/j.xops.2025.100911_bib42 article-title: Application and accuracy of artificial intelligence-derived large language models in patients with age related macular degeneration publication-title: Int J Retina Vitr doi: 10.1186/s40942-023-00511-7 – volume: 17 start-page: 4021 year: 2023 ident: 10.1016/j.xops.2025.100911_bib43 article-title: The utility of ChatGPT in diabetic retinopathy risk assessment: a comparative study with clinical diagnosis publication-title: Clin Ophthalmol Auckl NZ doi: 10.2147/OPTH.S435052 – volume: 5 year: 2025 ident: 10.1016/j.xops.2025.100911_bib12 article-title: Analysis of ChatGPT responses to ophthalmic cases: can ChatGPT think like an ophthalmologist? publication-title: Ophthalmol Sci doi: 10.1016/j.xops.2024.100600 – volume: 14 year: 2019 ident: 10.1016/j.xops.2025.100911_bib6 article-title: Systematic review on barriers and enablers for access to diabetic retinopathy screening services in different income settings publication-title: PLoS One doi: 10.1371/journal.pone.0198979 – volume: 178 year: 2024 ident: 10.1016/j.xops.2025.100911_bib17 article-title: A survey on advancements in image–text multimodal models: from general techniques to biomedical implementations publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2024.108709 |
| SSID | ssj0002513241 |
| Score | 2.3137317 |
| Snippet | To evaluate the diagnostic accuracy of 4 multimodal large language models (MLLMs) in detecting and grading diabetic retinopathy (DR) using their new image... ObjectiveTo evaluate the diagnostic accuracy of 4 multimodal large language models (MLLMs) in detecting and grading diabetic retinopathy (DR) using their new... Objective: To evaluate the diagnostic accuracy of 4 multimodal large language models (MLLMs) in detecting and grading diabetic retinopathy (DR) using their new... |
| SourceID | doaj proquest pubmed crossref elsevier |
| SourceType | Open Website Aggregation Database Index Database Publisher |
| StartPage | 100911 |
| SubjectTerms | Artificial intelligence Diabetic retinopathy Image analysis Multimodal large language model Ophthalmology Ultra-widefield fundus photography |
| Title | Can Multimodal Large Language Models Diagnose Diabetic Retinopathy from Fundus Photos? A Quantitative Evaluation |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S266691452500209X https://www.clinicalkey.es/playcontent/1-s2.0-S266691452500209X https://dx.doi.org/10.1016/j.xops.2025.100911 https://www.ncbi.nlm.nih.gov/pubmed/41030829 https://www.proquest.com/docview/3256110090 https://doaj.org/article/5a07eef880fa455b8e26860c17f4ac3f |
| Volume | 6 |
| WOSCitedRecordID | wos001576792300002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2666-9145 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002513241 issn: 2666-9145 databaseCode: DOA dateStart: 20210101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2666-9145 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002513241 issn: 2666-9145 databaseCode: M~E dateStart: 20210101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELagQohLxZsUqIzEDUXEsR3HJ1TKrji0VXlqb5bj2OoiSFbNLoILv50ZO1m1EtALl8iy7NiaGdufPS9CntfWoRi4vFSNzQWWbO1kLqQupfVwY4ga089H6uSkXiz06YVUX2gTlsIDJ8K9lLZQ3gcQs2CFlE3ty6quCsdUENbxgLtvofSFyxTuwXBqA1Jgo5dMMuj60a8wPncp0TJAM3bpJIoB-y8dSH8DnPHgmd8muyNipAdppnfINd_dJTePR534PbI6tB2NfrTf-hYaHqFtN3zTOyTFZGdfB_ommdR5mkxglo6-R2fnHjMS_6ToZELnG0zjQU_P-nU_vKIH9N3GdtEHDXZEOtuGBb9PPs1nHw_f5mMehdzJUq5zZ7nlvNXcaqa9DKJqQxNg5frCtYw7HSqhC6eroL1QWLJBBGslVLiKNfwB2en6zj8itGlKpV1QuvUAvRyzzvm6dU3ptQcu2Iy8mGhqVilchpnsyL4Y5IBBDpjEgYy8RrJvW2Ko61gBAmBGATBXCUBG-MQ0M3mTwv4HP1r-c2j1p15-GJfwYJgZSlOYD4BgKs2i-hegtV5kRG57jigloY8rR3w2SZSBJYx6Gdv5fjMYDrATI_fpIiMPk6htSSJYDCik9_4HqR6TWzCh8QXpCdlZn2_8U3LDfV8vh_N9cl0t6v24juB7_Gv2G-QWI34 |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Can+Multimodal+Large+Language+Models+Diagnose+Diabetic+Retinopathy+from+Fundus+Photos%3F+A+Quantitative+Evaluation&rft.jtitle=Ophthalmology+science+%28Online%29&rft.au=Most%2C+Jesse+A&rft.au=Walker%2C+Evan+H&rft.au=Mehta%2C+Nehal+N&rft.au=Nagel%2C+Ines+D&rft.date=2026-01-01&rft.eissn=2666-9145&rft.volume=6&rft.issue=1&rft.spage=100911&rft_id=info:doi/10.1016%2Fj.xops.2025.100911&rft_id=info%3Apmid%2F41030829&rft.externalDocID=41030829 |
| thumbnail_m | http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fcdn.clinicalkey.com%2Fck-thumbnails%2F26669145%2Fcov200h.gif |