Diagnostic Decision Support and Automation in Vestibular Diagnosis

This article examines the current and future role of decision-support systems and automation in vestibular diagnosis across emergency, primary care, and specialty settings. Vestibular disorders are common but often misdiagnosed because of overlapping symptoms, challenging bedside examinations, and limitations of neuroimaging. Structured diagnostic approaches improve accuracy when correctly applied. Emerging technologies offer objective assessment of eye movements and may enhance diagnostic reliability. Large language models provide valuable support for clinical reasoning, education, and information retrieval but remain vulnerable to hallucinations and incorrect inputs. Future progress will depend on robust clinical data capture, eye-movement repositories, and validated artificial intelligence-supported diagnostic workflows.