Development of a multimodal large language model for early warning and diagnosis of chronic ocular GVHD.

AI / informaticsNPJ digital medicine· Jun 2026

Development of a multimodal large language model for early warning and diagnosis of chronic ocular GVHD.

Shuwan Liu, Lili Cao, Haoran Wu … Jing Hong

Read paper

OphthalmologyAI / informaticsDiagnosisQ1IF 18.0

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 · Jun 2026 · Q1 in General Medicine

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%.

“

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.

Development of a multimodal large language model for early warning and diagnosis of chronic ocular GVHD.

Development of a multimodal large language model for early warning and diagnosis of chronic ocular GVHD.

Related papers