Redefining Elderly Care with Agentic AI: Challenges and Opportunities

2025-07-20

AI Summary

This study addresses critical challenges—including privacy preservation, decision transparency, safety assurance, and technological accessibility—in deploying large language model (LLM)-driven Agentic AI for elder care amid global population aging, particularly in health monitoring, cognitive support, and environmental adaptation. Methodologically, it conducts the first systematic review of Agentic AI’s capability boundaries and ethical risks in geriatric contexts, proposes human-centered design principles that balance autonomy with ethical values, and develops a prototype dashboard integrating personalized modeling, real-time health tracking, and interactive visualization. The contributions include: (1) filling a key research gap at the intersection of Agentic AI and gerontechnology; (2) identifying concrete technical and socio-technical bottlenecks hindering real-world deployment; and (3) providing both theoretical foundations and actionable implementation pathways toward trustworthy, equitable, and interpretable intelligent elder care systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The global ageing population necessitates new and emerging strategies for caring for older adults. In this article, we explore the potential for transformation in elderly care through Agentic Artificial Intelligence (AI), powered by Large Language Models (LLMs). We discuss the proactive and autonomous decision-making facilitated by Agentic AI in elderly care. Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older adults, represents important areas of application. With a potential for significant transformation of elderly care, Agentic AI also raises profound concerns about data privacy and security, decision independence, and access. We share key insights to emphasize the need for ethical safeguards, privacy protections, and transparent decision-making. Our goal in this article is to provide a balanced discussion of both the potential and the challenges associated with Agentic AI, and to provide insights into its responsible use in elderly care, to bring Agentic AI into harmony with the requirements and vulnerabilities specific to the elderly. Finally, we identify the priorities for the academic research communities, to achieve human-centered advancements and integration of Agentic AI in elderly care. To the best of our knowledge, this is no existing study that reviews the role of Agentic AI in elderly care. Hence, we address the literature gap by analyzing the unique capabilities, applications, and limitations of LLM-based Agentic AI in elderly care. We also provide a companion interactive dashboard at https://hazratali.github.io/agenticai/.

Problem

• Exploring Agentic AI's role in elderly care transformation
• Addressing privacy and ethical concerns in AI-driven elderly care
• Analyzing LLM-based AI capabilities and limitations for elderly care

Innovation

• Agentic AI enables autonomous elderly care decisions
• LLMs power personalized health and cognitive tracking
• Ethical safeguards ensure privacy in AI care