ArticleNPJ digital medicine2025
Multimodal machine learning enables AI chatbot to diagnose ophthalmic diseases and provide high-quality medical responses.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05930444 (Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases Using ChatGPT-based Natural Language Processing and Image Processing Techniques), which is not on this map. Cited by 22 papers.
What it found
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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.
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.
Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases Using ChatGPT-based Natural Language Processing and Image Processing Techniques
Who cites it
22 citing papers in PubMed.
- Comparison of Two AI Chatbots for Diagnosis and Providing Treatment Suggestions in Retinopathy of Prematurity: Retrospective Study.Journal of medical Internet research · 2026Article
- An autonomous multimodal AI agent for evidence-grounded ophthalmic diagnosis.Cell reports. Medicine · 2026Article
- Review
- Accuracy and Effectiveness of AI-Powered Systems in Patient Counseling, Education, and Management in Optometry and Related Eye-Care Settings: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Diagnostic accuracy and citation integrity of four large language models on otolaryngology vignettes.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Do Multimodal Vision-Language Models Enhance the Medical Diagnostic Process? A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Transforming Eye-Care Diagnostics Through Artificial Intelligence, Biometric Evaluation, and Tele-Optometry.Cureus · 2026Review
- Barriers and opportunities of scaling ambient AI scribes for clinical documentation across diverse healthcare settings.NPJ digital medicine · 2026Review
- Sequential sensitivity analysis of multimodal large language models for rare orbital disease detection.Communications medicine · 2026Article
- Article
- Accuracy, readability, and bias of GPT-4o mini responses to oculoplastic patient questions.Frontiers in ophthalmology · 2026Article
- Prediction of refractive error in adolescents using a multimodal large language model.Frontiers in medicine · 2026Article
- An extensive analysis of machine learning techniques for identifying glaucoma.International ophthalmology · 2025Review
- The rise and potential opportunities of large language model agents in bioinformatics and biomedicine.Briefings in bioinformatics · 2025Review
- Oxidative Stress and Inflammation in Hypoxemic Respiratory Diseases and Their Comorbidities: Molecular Insights and Diagnostic Advances in Chronic Obstructive Pulmonary Disease and Sleep Apnea.Antioxidants (Basel, Switzerland) · 2025Review
- Advancing Spine Fracture Detection: The Role of Artificial Intelligence in Clinical Practice.Korean journal of neurotrauma · 2025Review
- Embodied artificial intelligence in ophthalmology.NPJ digital medicine · 2025Review
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
- Multimodal artificial intelligence in medicine: a task-oriented framework for clinical translation.Frontiers in medicine · 2025Review
- Application prospect of large language model represented by ChatGPT in ophthalmology.International journal of ophthalmology · 2025Review
Corrections and comments
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Authors and funding
36 authors.
Funding
Abstract
Chatbot-based multimodal AI holds promise for collecting medical histories and diagnosing ophthalmic diseases using textual and imaging data. This study developed and evaluated the ChatGPT-powered Intelligent Ophthalmic Multimodal Interactive Diagnostic System (IOMIDS) to enable patient self-diagnosis and self-triage. IOMIDS included a text model and three multimodal models (text + slit-lamp, text + smartphone, text + slit-lamp + smartphone). The performance was evaluated through a two-stage cross-sectional study across three medical centers involving 10 subspecialties and 50 diseases. Using 15640 data entries, IOMIDS actively collected and analyzed medical history alongside slit-lamp and/or smartphone images. The text + smartphone model showed the highest diagnostic accuracy (internal: 79.6%, external: 81.1%), while other multimodal models underperformed or matched the text model (internal: 69.6%, external: 72.5%). Moreover, triage accuracy was consistent across models. Multimodal approaches enhanced response quality and reduced misinformation. This proof-of-concept study highlights the potential of chatbot-based multimodal AI for self-diagnosis and self-triage. (The clinical trial was registered on June 26, 2023, on ClinicalTrials.gov under the registration number NCT05930444.).
Identifiers
What OpenQuestion holds
Registered trials
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.