Smeriglio, Erika and Imbesi, Martina and Xiao, Jianbo and Smeriglio, Antonella and Trombetta, Domenico UNSPECIFIED (2026) Artificial intelligence and machine learning for precision prevention of cognitive decline: integrating multimodal biomarkers, lifestyle interventions, and natural medicines from prediction to clinical practice. Frontiers in Aging Neuroscience, 18. ISSN 1663-4365
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Abstract
Cognitive decline and neurodegenerative diseases are progressive, multifactorial conditions that may begin years before overt clinical diagnosis and reflect interactions among biological vulnerability, modifiable exposures, environmental determinants, and reduced brain resilience. This structured narrative review examines how artificial intelligence (AI) and machine learning (ML) can integrate clinical, biological, behavioral, and digital data to support earlier, personalized, and clinically actionable strategies for preserving cognitive health. Drawing on evidence from aging neuroscience, biomarker research, digital medicine, lifestyle prevention, natural-product pharmacology, and translational AI, we propose an AI-enabled framework for precision prevention and early management of cognitive decline. Within this framework, AI may support multimodal data integration, individualized risk prediction, digital phenotyping, biomarker-based stratification, intervention selection, natural-compound prioritization, and longitudinal monitoring. Lifestyle interventions and natural medicines are considered complementary components of personalized care whose value depends on biological plausibility, standardization, target engagement, and measurable cognitive or biomarker effects. However, translation from benchmark datasets to clinical practice remains limited by insufficient prospective and external validation, poor interpretability, dataset bias, limited generalizability, inadequate calibration and clinical-utility assessment, and incomplete integration into real-world workflows. Overall, AI may provide the integrative architecture needed to combine multimodal biomarkers, modifiable risk profiles, lifestyle interventions, and natural-product pharmacology within dynamic, person-centered precision-prevention pathways. Its clinical value will depend on transparent reporting, representative datasets, prospective evaluation, and demonstrable improvement in clinical decisions and patient outcomes.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence, clinical implementation, cognitive decline, machine learning, multimodal biomarkers, natural medicines, neurodegenerative diseases, precision prevention |
| Subjects: | Subjects > Biomedicine Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Articles and books |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 03 Sep 2026 09:24 |
| Last Modified: | 03 Sep 2026 09:24 |
| URI: | https://repositorio.funiber.org/id/eprint/29659 |
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