AI diagnostic tool detects chronic ocular graft-versus-host disease with high accuracy
Development of a multimodal large language model for early warning and diagnosis of chronic ocular GVHD.
Shuwan Liu … Jing Hong
NPJ digital medicine · 2026
Background
Chronic ocular graft-versus-host disease (coGVHD) after allogeneic hematopoietic stem cell transplantation (allo-HSCT) may lead to irreversible ocular surface damage and even vision loss.
Purpose
This study aimed to leverage a multimodal large language model (multimodal large language model) to develop an early warning and diagnostic system for coGVHD.
Methods
A total of 666 post-allo-HSCT patients (early warning model) and 805 post-allo-HSCT patients (1574 eyes, diagnostic model) were enrolled for construction, internal validation, and external validation of the corresponding models.
n = 1574 eyes
Results
how accurately the AI told coGVHD patients apart from those without it
AUROC 93.44 (95% CI 91.85 to 95.03)
CI excludes the null - significant
More results
Current management of coGVHD faces challenges, with frequent missed or misdiagnosed cases.
In external validation, the early warning AUROC was 83.45%, while diagnostic AUROCs across three external sites were all above 96.0%.
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Conclusion
The GVHD-MLLM can process rich multi-modal information collected in clinical practice, and is expected to become an effective tool for managing coGVHD.