Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases

Multimodal Machine Learning for Auxiliary Diagnosis of Eye Diseases Using ChatGPT-based Natural Language Processing and Image Processing Techniques

With rapid advancements in natural language processing and image processing, there is a growing potential for intelligent diagnosis utilizing chatGPT trained through high-quality ophthalmic consultation. Furthermore, by incorporating patient selfies, eye examination photos, and other image analysis techniques, the diagnostic capabilities can be further enhanced. The multi-center study aims to develop an auxiliary diagnostic program for eye diseases using multimodal machine learning techniques and evaluate its diagnostic efficacy in real-world outpatient clinics.

Inclusion Criteria:

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

Exclusion Criteria:

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