AI FOR PREVENTIVE HEALTHCARE, A DEEP LEARNING FRAMEWORK FOR EARLY DIAGNOSIS OF NEURODEGENERATIVE DISORDERS

Abstract

The integration of Artificial Intelligence (AI), particularly deep learning techniques, into preventive healthcare has opened new avenues for the early detection and management of neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases. These conditions, marked by progressive neuronal decline, affect millions globally and pose significant diagnostic challenges, especially in their early stages. AI-driven models, utilizing diverse datasets like medical imaging and electronic health records, have demonstrated the capacity to identify subtle patterns indicative of disease onset with greater speed and accuracy than traditional methods. This capability facilitates personalized and timely interventions, potentially altering the course of disease progression and improving patient outcomes. However, the adoption of AI in clinical practice also raises critical concerns, including data privacy, algorithmic bias, and the transparency of machine-generated decisions. Ethical considerations surrounding fairness and accountability continue to shape the discourse on responsible AI deployment in healthcare. As the field advances, emphasis on rigorous validation, cross- population reliability, and robust regulatory oversight remains essential to ensure safe and equitable implementation. This paper explores the transformative potential of AI in preventive neurology while addressing the technological, ethical, and practical challenges that must be navigated for its successful integration into real-world clinical settings.