What kind of intelligence belongs in aged care? Why values – not just data – must drive artificial intelligence adoption in aged care systems
Jane Barratt A *A
Abstract
What is known about the topic? This article explores the integration of artificial intelligence (AI) in aged care, emphasising that technology cannot substitute for systemic reform. While AI is already deployed to detect pain, predict falls, and reduce administrative burdens, its risks include bias, depersonalisation, and inequity when adopted without ethical guardrails. What does this paper add? The article proposes three guiding questions: who designs the AI and who is missing, what outcomes it optimises for, and whether it reduces or reinforces inequities. What are the implications for practitioners? The article concludes that AI should augment—not replace—care, ensuring dignity, equity, and human rights remain at the centre of aged care systems.
Setting the stage: where artificial intelligence meets aged care
Artificial intelligence (AI) is no longer speculative. It is embedded in health systems – and increasingly, in aged care. But the more urgent question is not whether AI is ready for long-term care, but whether long-term care is ready for AI.
AI is often presented as a solution to workforce shortages, cost pressures, and fragmented service delivery. In aged care, it is already being deployed to detect pain, automate documentation, predict falls, and even provide companionship. However, technology alone cannot remedy structural neglect. Its potential will only be realised if development and deployment are grounded in ethics, inclusivity, and the lived realities of those receiving and delivering care.
Australia has reached a pivotal point. AI can enhance aged care – but only if we stop treating it as a technological fix and begin treating it as a social, clinical, and policy opportunity.
This momentum is reflected in ongoing reforms overseen by the Department of Health, Disability and Ageing. Much of the foundational work – including the National Aged Care Data Strategy 2023–2025 – was developed under former Minister for Aged Care Anika Wells, with implementation of the 2024–2029 Strategy1 now continuing under Sam Rae, the Minister for Aged Care and Seniors. The Data Strategy emphasises the role of digital infrastructure and improved data systems in strengthening quality, equity, and accountability across the sector.
Readiness for AI in aged care is often measured by procurement or pilot implementation. But a more robust framework would consider:
Governance structures that prioritise transparency, equity, and lived experience
Investment in digital infrastructure, especially in rural and regional settings
Workforce capability development, ensuring digital tools complement rather than displace care relationships
While these systemic foundations remain uneven, practical examples of AI use in Australian aged care are increasing. PainChek®, a facial recognition tool that supports pain assessment in non-verbal individuals with dementia, is used in over 1800 facilities worldwide, including many in Australia. Its psychometric reliability has been independently validated,2 and the Australian Government supported its deployment through the Dementia and Aged Care Services Fund national trial in residential aged care facilities.
During the COVID-19 pandemic, Abi—a robotic companion developed by Andromeda Robotics—was introduced in Melbourne aged care homes to counter isolation and spark meaningful engagement with residents.3 Today innovation continues: platforms such as Minikai are transforming clinical documentation to ease administrative burdens,4 while research teams at Monash University are advancing AI systems that predict falls and monitor wellbeing, opening new possibilities ro improve safety, and quality in aged care.5,6
Framing the debate: what really matters in artificial intelligence integration
While promising, these examples reveal only part of the story. The more pressing questions relate to the assumptions, values, and design logics underpinning AI use in care.
Three questions that must guide artificial intelligence in aged care
1. Who designs the AI—and who is missing from the room?
If developers lack frontline experience—never having worked in aged care or supported someone with cognitive decline—there is a real risk that systems will entrench rather than redress systemic bias. A Monash University report found that AI systems can inadvertently encode ageism and cultural bias when older adults and minority communities are not represented in design and training datasets.
2. What is AI optimising for?
Many AI tools are engineered to maximise measurable outputs—falls avoided, tasks completed, time saved. Yet aged care is relational, not purely transactional. Quality of life indicators such as comfort, autonomy, or human connection are harder to quantify but no less essential. As many experts have argued, aged care policy must shift from a narrow focus on efficiency toward one that equally values wellbeing, dignity, and human-centred outcomes. 7
This framing also raises concern about the potential over-medicalisation of aged care, where human connection and psychosocial wellbeing are overlooked in favour of what is most easily measured.
3. Are we reducing inequities – or reinforcing them?
AI relies on data. When datasets exclude Aboriginal and Torres Strait Islander Elders, culturally and linguistically diverse populations, or those in remote areas, the resulting tools may misrepresent their needs. The World Health Organization 8 warns that biased data can amplify health inequities, especially when AI systems are trained on non-representative populations. In Australia, Monash University’s Artificial Intelligence in Aged Care report 5 echoes these concerns, stressing the need for inclusive design and representative data to avoid reinforcing existing disparities.
From risk to reform
There are risks. AI can depersonalise care, intensify surveillance, and erode trust – particularly when introduced without consultation or clear safeguards. However, when guided by values, AI can shift aged care from crisis response to prevention; from rote documentation to person-centred support.
It can assist in detecting subtle cognitive changes, translating care plans in real time (linguistically, culturally, or across platforms), and issuing alerts that help overburdened staff respond more effectively. However, policy leaders must continually ask: who benefits, who is accountable, and what trade-offs are being made?
Ethical governance demands more than compliance—it must embed regulatory clarity, uphold data sovereignty, and ensure meaningful co-design with aged care residents, families, and frontline staff.
This co-design must extend beyond token consultation to actively include carers, Aboriginal and Torres Strait Islander Elders, and culturally and linguistically diverse communities. Governance frameworks should not only reference but operationalise Australia’s AI Ethics Principles (2024),9 adapted to reflect the relational, human-centred realities of aged care.
Conclusion
The question is no longer whether AI will be adopted in aged care—it already is. The real test is whether its integration will serve the people at the heart of the system. This moment demands not just innovation, but intention: AI that is purposeful, inclusive, and rooted in human dignity.
We must not settle for automation: we must design for augmentation—with empathy, ethics, and equity. What is at stake is not just efficiency, but the rights of older people—human rights to age well.
References
1 Australian Government. Aged Care Data and Digital Strategy 2024-2029. 2024. Available at https://www.health.gov.au/sites/default/files/2024-07/aged-care-data-and-digital-strategy-2024-2029_0_0.pdf
2 Atee M, Hoti K, Parsons R, Hughes JD. Pain assessment in dementia: Evaluation of a point-of-care technological solution. J Alzheimers Dis 2017; 60(1): 137-150.
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3 Benetas. Benetas and Andromeda Robotics trial AI robot at Altona aged care home. Hawthorn (VIC): Benetas; 2024 Apr 21 [cited 2025 Sep 27]. Available at https://careers.benetas.com.au/blogs/blog-posts/benetas-and-andromeda-robotics-trial-ai-robot-at-altona-aged-care-home
4 Minikai. Minikai: Automating healthcare administration. 2024. Available at https://www.tidalvc.com/blog/investment-notes-minikai
5 Monash University. AI for Older Australians in Aged Care Facilities: Challenges and Opportunities. 2023. Available at https://research.monash.edu/en/projects/ai-for-older-australians-in-aged-care-facilities-challenges-and-o
6 Neves BB, Petersen A, Vered M, Carter A, Omori M. Artificial Intelligence in Long-Term Care: Technological Promise, Aging Anxieties, and Sociotechnical Ageism. Journal of Applied Gerontology 2023; 42(6): 1272-1282.
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7 Alsaeed T, Washington T, Xia B. Comprehensive analysis of Australia’s aged care system to inform policies for a sustainable future. Front Public Health 2025; 11(13): 1525988.
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8 World Health Organization. Ethics and governance of artificial intelligence for health. 2021. Available at https://www.who.int/publications/i/item/9789240029200
9 Australia’s Artificial Intelligence Ethics Principles. Department of Industry, Science and Resources. 2024 (updated 11 October). Available at https://www.industry.gov.au/publications/australias-artificial-intelligence-ethics-principles/australias-ai-ethics-principles
