ArticleSichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition2026
[Interdisciplinary Research on Artificial Intelligence and Longevity Medicine: Global Landscape, Collaboration Networks, and Frontier Trends (2010-2025)].
Article in Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: With the expansion of theories concerning aging and the rapid evolution of artificial intelligence (AI) methodologies, the integration of AI and longevity medicine has emerged as a critical pathway for extending human healthspan. This study is aimed at a systematic characterization of the research landscape, collaboration networks, and frontier trends in this interdisciplinary field, so as to inform clinical translation and the development of the health management industry. Methods: The Web of Science Core Collection, including Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI), was used as the data source. English-language publications in the interdisciplinary field of AI and longevity medicine between 2010 and 2025 were systematically retrieved, and 4170 valid records were included for a joint analysis using Bibliometrix and CiteSpace 7.0. Results: Annual publication output showed exponential growth. The global research landscape exhibited a China-US dual-core and a multipolar pattern, with institutional collaborations clustering into 3 major networks spanning Asia-Europe, the United States, and Continental Europe. Research topics were clustered into five major themes-the core concepts of aging and multimorbidity, AI-driven body composition and musculoskeletal assessment, deep-learning-based evaluation of brain aging through neuroimaging, wearable sensing and mobile health, and digital phenotyping and mental health. Marked differences in research priorities were observed among the three leading contributors, including China, the United States, and the United Kingdom. China demonstrated broad research coverage and clinically oriented multi-track development, the United States led in methodological innovation and digital phenotyping, and the United Kingdom concentrated on brain age research. Conclusions: This study systematically delineates the research landscape, collaboration networks, and frontier trajectories in the interdisciplinary field of AI and longevity medicine, providing data-driven evidence and decision-making support for advancing clinical translation and the development of the health management industry in this field.
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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 OpenQuestion graph.