# Redefining Elderly Care with Agentic AI: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2507.14912 · HTML: https://mlsanity.com/paper/2507.14912/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 3 of its 105 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 3 of 3)

- [Large Language Model Sentinel: LLM Agent for Adversarial Purification](https://mlsanity.com/paper/2405.20770/) (2024)
- [Adversarial Attacks on Large Language Models in Medicine](https://mlsanity.com/paper/2406.12259/) (2024)
- [Exploring the Role of LLMs for Supporting Older Adults: Opportunities and Concerns](https://mlsanity.com/paper/2411.08123/) (2024)

## Built on by (most cited first, 1 of 1)

- [Socio-technical aspects of Agentic AI](https://mlsanity.com/paper/2601.06064/) (2025)

## Abstract (arXiv)

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/.