Intelligent Social Welfare: How AI Optimizes Social Assistance, Elderly Care, and Healthcare Systems

Artificial Intelligence (AI) has emerged as a transformative force in social welfare systems, providing innovative solutions to enhance efficiency, accessibility, and equity. This paper examines AI applications in social assistance, elderly care, and healthcare, demonstrating how predictive analytics, automation, and data-driven decision-making optimize service delivery. The research also explores the ethical, legal, and governance challenges of AI integration, including algorithmic bias, data privacy, and transparency. Furthermore, international policy comparisons illustrate diverse approaches to AI-driven welfare models. The study concludes with future research directions, emphasizing the need for ethical frameworks and regulatory oversight to ensure AI-driven social welfare remains inclusive and effective.

Authors

Publication Details

Journal
Digital Society & Virtual Governance
Published
2025-02-20
DOI
https://doi.org/10.6914/dsvg.010102
Citations
3
Primary Topic
Insurance, Mortality, Demography, Risk Management
Type
article
Field-Weighted Citation Impact
16.78
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Intelligent Social Welfare: How AI Optimizes Social Assistance, Elderly Care, and Healthcare Systems

Qiang Sun
3 citations
Digital Society & Virtual Governance
Insurance, Mortality, Demography, Risk Management
16.78
article

Intelligent Social Welfare: How AI Optimizes Social Assistance, Elderly Care, and Healthcare Systems

Qiang Sun
article en
3 citations

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in social welfare systems, providing innovative solutions to enhance efficiency, accessibility, and equity. This paper examines AI applications in social assistance, elderly care, and healthcare, demonstrating how predictive analytics, automation, and data-driven decision-making optimize service delivery. The research also explores the ethical, legal, and governance challenges of AI integration, including algorithmic bias, data privacy, and transparency. Furthermore, international policy comparisons illustrate diverse approaches to AI-driven welfare models. The study concludes with future research directions, emphasizing the need for ethical frameworks and regulatory oversight to ensure AI-driven social welfare remains inclusive and effective.

Digital Society & Virtual GovernanceVol. 1(1)
Openalex Percentile: Top 1%
Insurance, Mortality, Demography, Risk Management
16.78
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.