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Multimodal Machine Learning Enables AI Chatbot to Diagnose Ophthalmic Diseases and Provide High-Quality Medical Responses: A Model Development and Multicenter Study

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Artificial intelligence and digital health in global eye health: opportunities and challenges
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Unveiling the clinical incapabilities: a benchmarking study of GPT-4V(ision) for ophthalmic multimodal image analysis
inferredcites →https://doi.org/10.1136/bjophthalmol-2022-321141openalex-snapshot
New meaning for NLP: the trials and tribulations of natural language processing with GPT-3 in ophthalmology
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Performance of ChatGPT on Chinese national medical licensing examinations: a five-year examination evaluation study for physicians, pharmacists and nurses
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A Multimodal Imaging–Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study
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The Effects of Health Care Chatbot Personas With Different Social Roles on the Client-Chatbot Bond and Usage Intentions: Development of a Design Codebook and Web-Based Study
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Uncovering Language Disparity of ChatGPT on Retinal Vascular Disease Classification: Cross-Sectional Study
inferredcites →https://doi.org/10.3389/fmed.2022.912214openalex-snapshot
Smartphone-Acquired Anterior Segment Images for Deep Learning Prediction of Anterior Chamber Depth: A Proof-of-Concept Study
inferredcites →https://doi.org/10.3389/frai.2023.1323924openalex-snapshot
Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protocol for a two-stage cross-sectional study
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Exploring Diagnostic Precision and Triage Proficiency: A Comparative Study of GPT-4 and Bard in Addressing Common Ophthalmic Complaints
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