This study aims to validate the clinical performance of an artificial intelligence (AI)-based automatic assessment system for the G-FAST score. The core comparison is the consistency and accuracy between AI-generated G-FAST results and standardized manual G-FAST assessments performed by trained professionals. The goal is to provide a convenient, efficient, and objective tool for acute stroke screening and early identification, reduce the subjective variability of manual scoring, and optimize the pre-hospital and in-hospital stroke assessment workflow.

Inclusion Criteria:

1. Aged ≥ 18 years, of either sex.
2. Clinically diagnosed with stroke, and confirmed by cranial CT/MRI to have ischemic or hemorrhagic stroke.
3. Onset within 7 days.
4. Alert and oriented, able to cooperate with standardized video and audio data collection.
5. The patient or their legally authorized representative understands the study and voluntarily provides written informed consent (including consent for audio-visual data collection).

Exclusion Criteria:

1. Neurological deficits caused by non-stroke etiologies (e.g., brain tumor, traumatic brain injury, encephalitis).
2. Patients with impaired consciousness, severe cognitive dysfunction, or psychiatric disorders that prevent cooperation with video collection and scale assessment.
3. Patients with severe visual or hearing impairment, or global aphasia, who are unable to follow instructions.
4. Critically ill patients requiring immediate cardiopulmonary resuscitation or endotracheal intubation, making video and audio data collection impossible.
5. Patients with severe facial or limb deformities, or large-area dressings that severely interfere with camera data collection.
6. Patients with unilateral or bilateral upper limb amputation, severe deformity, unhealed fracture, joint fixation, or severe contracture.

Patients with acute ischemic stroke who undergo G-FAST scale assessments using both AI and manual methods.

Due to the protection of participant privacy and institutional review board requirements, individual participant data (IPD) will not be shared publicly.