人工智能与长寿医学交叉研究：全球格局、合作网络与前沿趋势（2010–2025）

Interdisciplinary Research on Artificial Intelligence and Longevity Medicine: Global Landscape, Collaboration Networks, and Frontier Trends (2010–2025)

摘要

目的 随着衰老理论的拓展与人工智能（AI）方法学的快速演进，AI与长寿医学的交叉融合已成为延长人类健康寿命的关键路径。本研究旨在系统揭示该交叉领域的研究格局、合作网络与前沿趋势，为其临床转化与健康管理产业发展提供依据。方法 本研究以Web of Science核心合集（SCIE+SSCI）为数据源，系统检索2010–2025年间AI与长寿医学交叉领域的英文文献，共纳入4 170篇有效文献，采用Bibliometrix与CiteSpace 7.0联合分析策略。结果 该领域发文量呈指数型增长；全球格局形成中美双核心、多极化态势，机构合作划分为亚欧、美国与欧陆三大网络；研究主题汇聚为衰老与多病共存核心概念、AI驱动的身体成分与肌骨评估、深度学习驱动的神经影像衰老评估、可穿戴传感与移动健康、数字表型与心理健康五个研究主题聚类；中、美、英三国研究侧重点存在明显差异：中国呈广覆盖、临床导向多线并进特征，美国深耕方法学与数字表型，英国聚焦于脑龄方向。结论 本研究系统勾勒出AI与长寿医学交叉领域的研究格局、合作网络与前沿趋势，可为该领域的临床转化及健康管理产业发展提供数据支撑与决策参考。

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 4 170 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.

关键词

人工智能 / 长寿医学 / 老年科学 / 衰老生物标志物 / 脑龄 / 数字表型 / CiteSpace

Key words

Artificial intelligence / Longevity medicine / Geroscience / Aging biomarkers / Brain age / Digital phenotyping / CiteSpace

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