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An integrated language-vision foundation model for conversational diagnostics and triaging in primary eye care

We present Meta-EyeFM, an integrated language-vision foundation model designed for conversational diagnostics and triaging in primary eye care. By combining a large language model (LLM) with eight task-specific vision foundation models (VFMs), Meta-EyeFM dynamically routes user queries and fundus ph...

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Hauptverfasser: Soh, Zhi Da (VerfasserIn) , Bai, Yang (VerfasserIn) , Yu, Kai (VerfasserIn) , Zhou, Yang (VerfasserIn) , Lei, Xiaofeng (VerfasserIn) , Thakur, Sahil (VerfasserIn) , Lee, Zann (VerfasserIn) , Phang, Lee Ching Linette (VerfasserIn) , Peng, Qingsheng (VerfasserIn) , Xue, Can Can (VerfasserIn) , Chong, Rachel Shujuan (VerfasserIn) , Hoang, Quan V. (VerfasserIn) , Raghavan, Lavanya (VerfasserIn) , Tham, Yih Chung (VerfasserIn) , Sabanayagam, Charumathi (VerfasserIn) , Wu, Wei-Chi (VerfasserIn) , Ho, Ming-Chih (VerfasserIn) , He, Jiangnan (VerfasserIn) , Gupta, Preeti (VerfasserIn) , Lamoureux, Ecosse (VerfasserIn) , Saw, Seang Mei (VerfasserIn) , Nangia, Vinay (VerfasserIn) , Panda-Jonas, Songhomitra (VerfasserIn) , Xu, Jie (VerfasserIn) , Wang, Ya Xing (VerfasserIn) , Xu, Xinxing (VerfasserIn) , Jonas, Jost B. (VerfasserIn) , Wong, Tien Yin (VerfasserIn) , Goh, Rick Siow Mong (VerfasserIn) , Liu, Yong (VerfasserIn) , Cheng, Ching-Yu (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 16 December 2025
In: Cell reports. Medicine
Year: 2025, Jahrgang: 6, Heft: 12, Pages: 1-15
ISSN:2666-3791
DOI:10.1016/j.xcrm.2025.102476
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.xcrm.2025.102476
Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S266637912500549X
Volltext
Verfasserangaben:Zhi Da Soh, Yang Bai, Kai Yu, Yang Zhou, Xiaofeng Lei, Sahil Thakur, Zann Lee, Lee Ching Linette Phang, Qingsheng Peng, Can Can Xue, Rachel Shujuan Chong, Quan V. Hoang, Lavanya Raghavan, Yih Chung Tham, Charumathi Sabanayagam, Wei-Chi Wu, Ming-Chih Ho, Jiangnan He, Preeti Gupta, Ecosse Lamoureux, Seang Mei Saw, Vinay Nangia, Songhomitra Panda-Jonas, Jie Xu, Ya Xing Wang, Xinxing Xu, Jost B. Jonas, Tien Yin Wong, Rick Siow Mong Goh, Yong Liu, and Ching-Yu Cheng
Beschreibung
Zusammenfassung:We present Meta-EyeFM, an integrated language-vision foundation model designed for conversational diagnostics and triaging in primary eye care. By combining a large language model (LLM) with eight task-specific vision foundation models (VFMs), Meta-EyeFM dynamically routes user queries and fundus photographs to the most appropriate VFMs (accuracy 96.8%). It demonstrates high performance in detecting ocular diseases (area under the receiver operating curve [AUC] ≥91.2%), differentiating disease severity (AUC ≥82%), identifying ocular signs (AUC ≥77.9%), and predicting systemic conditions like diabetes (AUC ≥79.8%). Meta-EyeFM is 11%-43% more accurate than Gemini-1.5-flash and GPT-4o LLM and generally outperforms junior ophthalmologist and optometrist graders in detecting different eye diseases. Its conversational interface and robust generalizability support its role as a diagnostic decision support tool in community settings. Through self-supervised learning and a user-friendly platform, Meta-EyeFM addresses the scarcity of skilled eye care professionals, offering scalable, explainable AI for enhancing vision screening and disease triage globally.
Beschreibung:Online verfügbar: 4. Dezember 2025, Artikelversion: 16. Dezember 2025
Gesehen am 13.03.2026
Beschreibung:Online Resource
ISSN:2666-3791
DOI:10.1016/j.xcrm.2025.102476

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