Exploring the Applications of Artificial Intelligence in Elderly Mental Health Management

As global aging accelerates, mental health issues among the elderly-such as depression, anxiety, and loneliness-are emerging as critical public health concerns, significantly impairing their quality of life and social functioning. Recent advancements in artificial intelligence (AI) offer promising solutions for addressing these challenges within the domain of elderly mental health care. This study explores the applications, benefits, and limitations of AI in managing elderly mental health. Drawing upon an extensive review of current literature, case studies, and empirical data from interviews and questionnaires, the research reveals that AI technologies-including chatbots, emotion recognition systems, and wearable devices-have the potential to provide personalized, accessible, and scalable solutions for psychological assessment, emotional support, and daily companionship. The findings suggest that AI-driven interventions can effectively reduce symptoms of depression, alleviate feelings of loneliness, and enhance overall life satisfaction among older adults. However, the study also identifies several barriers to widespread adoption, including limited technological literacy, concerns about privacy and data security, and the digital divide. These challenges underscore the need for interdisciplinary collaboration, technological refinement, user-centered design, and policy development to ensure the sustainable integration of AI in elderly mental health care.

Authors

Publication Details

Journal
International Journal of Education and Humanities
Published
2025-05-14
DOI
https://doi.org/10.54097/0ng7x934
Citations
1
Primary Topic
Digital Mental Health Interventions
Type
article
Field-Weighted Citation Impact
1.54
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring the Applications of Artificial Intelligence in Elderly Mental Health Management

Shaoqing Wang
1 citations
International Journal of Education and Humanities
Digital Mental Health Interventions
1.54
article

Exploring the Applications of Artificial Intelligence in Elderly Mental Health Management

Shaoqing Wang
article en
1 citations

Abstract

As global aging accelerates, mental health issues among the elderly-such as depression, anxiety, and loneliness-are emerging as critical public health concerns, significantly impairing their quality of life and social functioning. Recent advancements in artificial intelligence (AI) offer promising solutions for addressing these challenges within the domain of elderly mental health care. This study explores the applications, benefits, and limitations of AI in managing elderly mental health. Drawing upon an extensive review of current literature, case studies, and empirical data from interviews and questionnaires, the research reveals that AI technologies-including chatbots, emotion recognition systems, and wearable devices-have the potential to provide personalized, accessible, and scalable solutions for psychological assessment, emotional support, and daily companionship. The findings suggest that AI-driven interventions can effectively reduce symptoms of depression, alleviate feelings of loneliness, and enhance overall life satisfaction among older adults. However, the study also identifies several barriers to widespread adoption, including limited technological literacy, concerns about privacy and data security, and the digital divide. These challenges underscore the need for interdisciplinary collaboration, technological refinement, user-centered design, and policy development to ensure the sustainable integration of AI in elderly mental health care.

International Journal of Education and HumanitiesVol. 19(2)
Openalex Percentile: Top 17%
Digital Mental Health Interventions
1.54
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.