ArticleFrontiers in cell and developmental biology2025
Multimodal reasoning agent for enhanced ophthalmic decision-making: a preliminary real-world clinical validation.
Article in Frontiers in cell and developmental biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- An autonomous multimodal AI agent for evidence-grounded ophthalmic diagnosis.Cell reports. Medicine · 2026Article
- Retrieval‑augmented large language models for depression screening and suicide risk stratification.BMC psychiatry · 2026Article
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
- Comparing Artificial intelligence to physicians' competences in the domain of clinical reasoning: A systematic review and meta-analysis.Journal of medical education and curricular developmentReview
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8 authors.
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Abstract
Although large language models (LLMs) show significant potential in clinical practice, accurate diagnosis and treatment planning in ophthalmology require multimodal integration of imaging, clinical history, and guideline-based knowledge. Current LLMs predominantly focus on unimodal language tasks and face limitations in specialized ophthalmic diagnosis due to domain knowledge gaps, hallucination risks, and inadequate alignment with clinical workflows. This study introduces a structured reasoning agent (ReasonAgent) that integrates a multimodal visual analysis module, a knowledge retrieval module, and a diagnostic reasoning module to address the limitations of current AI systems in ophthalmic decision-making. Validated on 30 real-world ophthalmic cases (27 common and 3 rare diseases), ReasonAgent demonstrated diagnostic accuracy comparable to ophthalmology residents (
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