Early Detection of Neurodegenerative Disorders Using Artificial Intelligence

Early identification of neurodegenerative disorders remains a major clinical challenge, as many conditions progress silently before noticeable symptoms appear. Recent advances in artificial intelligence (AI) have opened new opportunities to recognize subtle changes in behaviour, cognition, imaging patterns, and biological markers long before conventional diagnostic methods can detect them. This study reviews emerging AI-based approaches for the early detection of disorders such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. It highlights how machine learning and deep learning models can analyse multimodal data, including MRI scans, speech signals, gait patterns, and blood-based biomarkers, to reveal early pathological signatures with improved accuracy and speed. The paper also discusses practical considerations for integrating AI diagnostic tools into routine clinical workflows, including data quality, model interpretability, and ethical safeguards. Overall, the findings suggest that AI-driven early detection systems hold strong potential to support clinicians in making timely interventions, improving patient outcomes, and reducing long-term healthcare burdens.