Integrating Machine Learning and Advanced Technologies for Enhanced Prediction and Treatment Strategies in Dementia and Related Diseases - Mane, Pradip Suresh; Appa, M.A.Y. Peer Mohamed; Talekar, Priyanka S; Mallick, Mohamed; M Desai, Sudhanva; Jevin, J. A. (Journal of Neonatal Surgery, 2025)
Dementia and its accompanying neurodegenerative diseases represent some of the most challenging clinical problems and require new solutions for early detection and individualized therapy. In this research, various Machine Learning (ML) approaches and advanced technology integration are evaluated, which is a crucial aspect for improving disease diagnosis, monitoring, and treatment strategies, especially in the context of dementia management. According to the research, all supervised, unsupervised, and deep learning algorithms lead to the integration of large demographic data sets such as neuroimaging biomarkers and electronic health records, which will ultimately contribute to not only diagnosing diseases more accurately but also predicting their progression. This research utilizes a mixed-method approach to gather survey data from 300 participants, including patients, caregivers, and healthcare providers. The study also found that there was strong interest in gap-filling AI-powered tools despite the barriers of digital literacy, cost, and data privacy concerns. In this work, these researchers aim to propose a framework by which existing ML models are integrated with the flow of patient-related data, to facilitate clinical decision-making and personalized interventions. It ultimately describes interdisciplinary collaboration, ethical safeguards, and accessible technological infrastructure as a means of achieving what AI can do in dementia care. This study signals a shift towards proactive, person-centered, technology-facilitated healthcare for older people.