ReviewFrontiers in aging2026
Artificial intelligence and machine learning in immunosenescence: from biomarker discovery to clinical translation.
Review in Frontiers in aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The global population is undergoing unprecedented aging, with immunosenescence established as a core upstream driver of nearly all age-related chronic diseases, imposing a massive global clinical burden. For decades, immunosenescence research has been mired in three persistent translational bottlenecks: inability to capture interindividual immune aging heterogeneity, failure to decode complex multi-layered biological regulatory networks, and inefficient therapeutic development pipelines. Artificial intelligence (AI) and machine learning (ML) have emerged as promising solutions to these bottlenecks, yet existing literature fails to systematically bridge AI technical advances with clinical immunology practice, and often does not distinguish proof-of-concept evidence from the steps needed for clinical use. This review provides a holistic, critical overview of AI/ML applications across the full translational spectrum of immunosenescence research, from mechanistic discovery, biomarker development, diagnostic innovation to therapeutic development and personalized medicine. We further analyze unresolved technical, ethical, and regulatory barriers to clinical translation, including underrecognized fundamental flaws in current model design. Emerging AI technologies to address these limitations are outlined, alongside a clinically realistic translational roadmap and key future research trends. This review fills critical gaps in existing literature, providing a rigorous framework to shift the field from "AI for AI's sake" to clinical utility-focused research, ultimately advancing healthy aging interventions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.