Artificial intelligence and machine learning in the early detection and prediction of Alzheimer’s disease

Abstract Alzheimer’s disease (AD) remains one of the most pressing public health challenges of the 21st century, characterized by ­insidious onset, progressive cognitive decline, and profound socioeconomic burden. Conventional diagnostic approaches often identify AD at advanced stages, limiting the effectiveness of therapeutic interventions. Recent advances in artificial intelligence (AI) and machine learning (ML) offer transformative potential in the early detection and prediction of AD by leveraging vast, complex datasets spanning neuroimaging, genetic profiles, electrophysiological signals, and clinical assessments. ­AI-driven algorithms excel at pattern recognition, enabling the identification of subtle biomarkers and prodromal indicators long before clinical symptoms manifest. Moreover, predictive models powered by ML facilitate risk stratification, individualized prognosis, and precision-guided interventions, laying the groundwork for timely disease-modifying strategies. Integrating multimodal data from MRI and PET scans with cerebrospinal fluid biomarkers and digital cognitive tests further enhances diagnostic accuracy and predictive power. Despite these advancements, challenges persist, including data heterogeneity, algorithmic transparency, ethical considerations, and the need for rigorous clinical validation. Nonetheless, the convergence of neuroscience, computational modelling, and digital health promises a paradigm shift in the management of Alzheimer’s disease. Harnessing AI and ML not only holds the potential to significantly redefine early detection but also to reshape research, clinical care, and policymaking in the fight against this devastating disorder.