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Artificial intelligence across the aging continuum: Mechanistic geroscience, therapeutic innovation, and clinical impact.

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Τίτλος: Artificial intelligence across the aging continuum: Mechanistic geroscience, therapeutic innovation, and clinical impact.
Συγγραφείς: Li H; Department of Clinical Medicine, HuanKui Academy, Nanchang University, Nanchang, China., Abhulimen PO; China-Europe Research Ecosystem for Advanced Therapeutics Enterprise, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China., Yu Q; China-Europe Research Ecosystem for Advanced Therapeutics Enterprise, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. Electronic address: q.yu@siat.ac.cn., Cheng W; Translational Medicine R&D Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. Electronic address: wx.cheng@siat.ac.cn., Njunge LW; China-Europe Research Ecosystem for Advanced Therapeutics Enterprise, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. Electronic address: wn.lucy@siat.ac.cn.
Πηγή: Ageing research reviews [Ageing Res Rev] 2026 Aug; Vol. 120, pp. 103205. Date of Electronic Publication: 2026 Jun 10.
Τύπος έκδοσης: Journal Article; Review
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier Science Country of Publication: England NLM ID: 101128963 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-9649 (Electronic) Linking ISSN: 15681637 NLM ISO Abbreviation: Ageing Res Rev Subsets: MEDLINE
Imprint Name(s): Original Publication: Oxford, UK : Elsevier Science, c2002-
Ιατρικοί όροι (MeSH): Aging*/physiology , Artificial Intelligence*/trends , Geroscience*/methods , Geroscience*/trends , Translational Research, Biomedical*/methods , Translational Research, Biomedical*/trends, Biomarkers/analysis ; Humans
Περίληψη: Aging emerges from nonlinear interactions among primary, antagonistic, and integrative hallmarks that progressively erode tissue resilience. As global demographics shift and chronic disease burden intensifies, extending healthspan with mechanistic precision has become imperative, accelerating the incorporation of artificial intelligence into geroscience. AI leverages multi-omics, spatial biology, imaging, and clinical data to reveal nonlinear structures linking hallmark interactions to tissue vulnerability and organismal decline. These mechanistic insights inform target prioritization, perturbation-based pathway modeling, and rational design of multi-target geroprotectors, including compounds already advancing through clinical trials. Beyond discovery, AI supports synthetic data generation, cross-disease repurposing, and personalized geriatric care through digital phenotyping and predictive analytics. However, these advances hinge on confronting fundamental challenges in data quality, confounding variables, batch effects, and technical artifacts that risk encoding spurious correlations, necessitating hierarchical experimental validation and explainable AI to distinguish causal mechanisms from epiphenomena. Algorithmic bias, digital ageism, privacy vulnerabilities, and infrastructural inequalities further threaten to exacerbate disparities among vulnerable aging populations. This review uniquely traces AI across the complete translational continuum, from hallmark-grounded biomarker discovery to clinical deployment, while positioning validation rigor and ethical infrastructure as core scientific determinants of a transformative AI-geroscience ecosystem.
(Copyright © 2026 Elsevier B.V. All rights reserved.)
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Artificial Intelligence; Biological Aging; Biomarker and Drug Discovery; Clinical Intervention; Geriatric Healthcare; Geroscience
Substance Nomenclature: 0 (Biomarkers)
Entry Date(s): Date Created: 20260610 Date Completed: 20260729 Latest Revision: 20260729
Update Code: 20260729
DOI: 10.1016/j.arr.2026.103205
PMID: 42269726
Βάση Δεδομένων: MEDLINE
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  Data: Aging emerges from nonlinear interactions among primary, antagonistic, and integrative hallmarks that progressively erode tissue resilience. As global demographics shift and chronic disease burden intensifies, extending healthspan with mechanistic precision has become imperative, accelerating the incorporation of artificial intelligence into geroscience. AI leverages multi-omics, spatial biology, imaging, and clinical data to reveal nonlinear structures linking hallmark interactions to tissue vulnerability and organismal decline. These mechanistic insights inform target prioritization, perturbation-based pathway modeling, and rational design of multi-target geroprotectors, including compounds already advancing through clinical trials. Beyond discovery, AI supports synthetic data generation, cross-disease repurposing, and personalized geriatric care through digital phenotyping and predictive analytics. However, these advances hinge on confronting fundamental challenges in data quality, confounding variables, batch effects, and technical artifacts that risk encoding spurious correlations, necessitating hierarchical experimental validation and explainable AI to distinguish causal mechanisms from epiphenomena. Algorithmic bias, digital ageism, privacy vulnerabilities, and infrastructural inequalities further threaten to exacerbate disparities among vulnerable aging populations. This review uniquely traces AI across the complete translational continuum, from hallmark-grounded biomarker discovery to clinical deployment, while positioning validation rigor and ethical infrastructure as core scientific determinants of a transformative AI-geroscience ecosystem.<br /> (Copyright © 2026 Elsevier B.V. All rights reserved.)
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  Data: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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