OphAgent: A Generalisable Ophthalmic Agentic System for Global Eye Care

Artificial intelligence holds the potential to transform ophthalmic assessment, yet its translation into clinical practice remains hindered by its limited ability to perform complex reasoning, utilize specialized tools, or provide transparent justifications. We introduce OphAgent, a multi-agent framework powered by large language models that mimics clinical workflows through dynamic diagnostic planning, knowledge base consultation, and multimodal image interpretation via structured inter-agent debate. Validated across four imaging modalities, 30+ conditions, and 11 diverse real-world datasets, OphAgent achieved 85% average accuracy in retinal diagnosis, surpassing current state-of-the-art multimodal large language models. In a large-scale global reader study with 311 ophthalmologists from 86 centres across 22 countries, spanning seven languages and settings from tertiary hospitals to resource-limited clinics, OphAgent improved diagnostic accuracy by up to 20% across 14 clinically significant tasks. Critically, these gains remained stable across geographic regions, experience levels, and languages, demonstrating robustness, fairness, and generalisability. OphAgent exemplifies an emerging paradigm where medical AI evolves from isolated prediction to collaborative clinical reasoning. By integrating reasoned clinical consultation with state-of-the-art diagnostic performance, the framework offers a scalable pathway toward more equitable, transparent, and trustworthy AI-enabled clinical decision support, with the potential to extend expert-level ophthalmic assessment across diverse healthcare settings.