Ophthalmic emergencies are acute vision-threatening disorders, for which a delay in prompt emergency response could result in catastrophic vision loss. Triage is an effective process for ensuring that timely emergency care is provided despite limited resource by prioritizing patients to appropriate orders for visits. Historically, registered nurses classify emergency patients based on personal experiences with high variation. Additionally, primary healthcare providers have been conventionally at the forefront of providing first aid care. However, most of ocular emergencies are wrongly diagnosed or referred due to non-eye specialists' limited knowledge and training in the ophthalmology.

Here, the investigators established and validated an artificial intelligence system, EE-Explorer, to triage eye emergencies and assist in primary diagnosis using metadata and ocular images. This system has been integrated into a website to be prospectively validated in the real world.

Inclusion Criteria:

1. Suffering acute ophthalmic symptoms within one month
2. Visiting the ocular emergency department for the first time
3. Must be able to complete the triage form for ophthalmic emergency
4. Must be able to cooperate either by submitting smartphone photographs or receiving slit-lamp examination

Exclusion Criteria:

The image quality does not meet the clinical requirements.

Through the online popular science, news reports, and other channels, we will promote and inform patients about the relevant knowledge of ophthalmic emergencies, so that they can judge by themselves and freely decide whether to participate in this study or not.