Abstract
Early detection of neurodegenerative diseases like Alzheimer’s and Parkinson’s is crucial for improving patient care and enabling timely interventions. Artificial intelligence (AI) offers innovative approaches to analyzing complex medical datasets, revolutionizing the detection of these diseases at early stages. This review discusses key AI methodologies, including machine learning (ML), deep learning (DL), natural language processing (NLP), and reinforcement learning (RL), and their applications in early diagnosis. ML models excel in predicting disease risk and classifying imaging and biometric data, while DL techniques, such as convolutional and recurrent neural networks, are effective in processing unstructured data like images and speech. NLP facilitates extracting critical insights from clinical notes and patient narratives, and RL enhances decision-making in diagnostic workflows. Integrating multimodal data—such as genomics, neuroimaging, wearable device metrics, and electronic health records—further strengthens diagnostic precision. Despite its promise, the widespread implementation of AI faces challenges, including the need for standardized data, ethical considerations, and clinical validation. Overcoming these obstacles is essential for AI to transform early detection and management of neurodegenerative diseases. This review emphasizes the significance of interdisciplinary efforts and sustained research to unlock AI’s full potential in medical applications.
Published Version
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