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

## TL;DR

This study explores how artificial intelligence development in China may help reduce depressive symptoms in older adults by improving economic and social conditions.

## Contribution

The study provides empirical evidence linking AI development to reduced depression in older adults through macroeconomic and micro-level mediators.

## Key findings

- AI development is significantly associated with reduced depressive symptoms in older adults.
- Factors like Internet access, robot density, and tech investment mediate AI's mental health benefits.
- Life satisfaction and cognitive function also contribute to AI's positive impact on depression.

## 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.