Authors : S. Gopal Krishna Patro, Rabinarayan Panda, Saptaparni Chatterjee, Jnanaranjan Mohanty, Satrujit Mishra, Khayala Mammadova, Mekhrbonu Rakhimova, Maytham T. Qasim, Mohammad Khishe
DOI : 10.1016/j.ibmed.2026.100423
Volume : 15
Issue : 1
Year : 2026
Page No : 100423
Alzheimer disease is a progressive neurodegenerative disease which primarily impairs the memory, cognitive abilities as well as behavioral functions. It is the leading cause of dementia and an important health issue in the world with the rising population of the elderly. The early diagnosis of the Alzheimer disease is essential to slow down the development of the disease and secure better outcome in patients though the traditional diagnostic systems tend to reveal the disease when it has already led to serious loss of neurons. The recent breakthroughs in the biomedical field and computer technologies have allowed creating data-driven methods of early diagnosis and disease monitoring. Specifically, artificial intelligence methods have shown to have an exceptional potential in the analysis of complex biomedical data, such as neuroimaging scans, biomarker measurements and clinical records. This work is a detailed analytical structure of research on the problem of Alzheimer disease based on artificial intelligence and multimodal biomedical data. The research incorporates the neuroimaging data, biochemical biomarkers as well as clinical parameters to create predictive models with which one can detect the early pathological alterations that relate to the disease. The machine learning and AI algorithms are used to process structural magnetic resonance imaging data and biomarker characteristics using large-scale research datasets. Mathematical modelling and statistical evaluation method is also included in the proposed framework to determine the classification performance and diagnostic reliability. As per experimental analysis, AI-based methods can substantially improve the diagnostic accuracy and allow obtaining an earlier diagnosis than the traditional ones. The results of this study bring out the possibility of the use of artificial intelligence technology and biomedical data analysis to assist clinicians in decision-making and to enhance the treatment of neurodegenerative diseases.