Multimodal Deep Learning Model for Multi-task Diagnosis and Triage Suggestions of Ophthalmic Diseases

Development and Validation of Multimodal Deep Learning Model for Autonomous Diagnosis, Generative Reporting, and Specialist Referral in Ophthalmic Diseases: An International Multicenter Cohort Study

Accurate and comprehensive interpretation of anterior segment diseases from slit-lamp and smartphone photographs remains a clinical challenge due to the limited specificity and structure of existing Artificial Intelligence tools. The purpose of this international, multicenter clinical trial is to developed and validated an agent-based framework that integrates vision-language models and large language models to enhance the diagnostic workflow of anterior segment diseases.

Inclusion Criteria:

1. Informed consent obtained;
2. Participants should be sufficiently able to read, write, and understand Chinese or English;
3. For normal participants: individuals should have no concerns related to their eyes.
4. For participants with eye-related chief complaints: individuals should have specific concerns or issues related to their eyes.

Exclusion Criteria:

1. Incomplete clinical data to support final diagnosis;
2. Patients who, in the opinion of the attending physician or clinical study staff, are too medically unstable to participate in the study safely.