Evidence map›Paper›PMID 41783308›Full record

ArticleFrontiers in psychology2026

The potential impacts of regional artificial intelligence development on depressive symptoms in older adults: evidence from China.

Shenwei Wan, Yixiao Liang, Zhiwen Ding, Yong Tang, Liangshan Yang

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Shenwei Wan *Institute of Rural Development, Zhejiang Academy of Agricultural Sciences, Zhejiang, China.
Yixiao Liang *School of Economics and Management, Northeast Forestry University, Harbin, China.
Zhiwen Ding *Institute of Rural Development, Zhejiang Academy of Agricultural Sciences, Zhejiang, China.
Yong TangInstitute of Rural Development, Zhejiang Academy of Agricultural Sciences, Zhejiang, China.
Liangshan YangInstitute of Rural Development, Zhejiang Academy of Agricultural Sciences, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Depression is increasingly prevalent among older adults worldwide, exacerbated in the post-pandemic era and driven by aging populations, economic strain, and quality-of-life declines. In China, these factors contribute significantly to arise in depression among this demographic. Meanwhile, Artificial Intelligence (AI) shows growing promise in mental health management, potentially offering valuable tools to mitigate depression. This study examines AI's capacity to alleviate depressive symptoms in older adults from a macroeconomic perspective, particularly in aging societies like China and other developing nations. Using data from the China Health and Retirement Longitudinal Study (CHARLS) spanning 2011-2020, employ a two-way fixed-effects model to empirically analyze AI's impact on depression in this demographic. Our results indicate a significant negative association between AI development and depressive symptoms among older adults. Mediation analysis reveals that macroeconomic factors, such as increased Internet access, robot application density, and investment in science and technology, and micro-level factors, like life satisfaction and cognitive function, contribute to AI's beneficial impact on mental health. While our findings are robust, limitations include data constraints and the need for further exploration of specific AI applications on depression outcomes. Future research could focus on interdisciplinary approaches integrating AI with psychomedical technologies, emphasizing support for vulnerable groups, including those in rural or under-resourced areas, and fostering public awareness and accessibility of AI health tools.

Indexed as

artificial intelligencedepressive symptomsheterogeneityolder adultstwo-way fixed effect

Identifiers

PMID41783308
PMCPMC12953406

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.