Ageing populations are putting structural pressure on health and care systems the world over. In Japan, nearly 35 per cent of the population is projected to be over 65 by 2040. The UK is following a similar trajectory, with over-65s rising from roughly 20 per cent today to around 25 per cent by 2040.
These demographic shifts are contributing to demand for health services that increasingly outpaces capacity. Without productivity gains, current models of care will struggle to remain sustainable.
In this context, incorporating artificial intelligence into health and care systems is moving from abstract promise to operational necessity. It has already begun being deployed to alleviate strain. For example, AI tools are increasingly able to detect signs of emerging conditions in patients by identifying early signals across large data sets, allowing clinicians to intervene before conditions escalate and shifting care from reactive treatment to preventative management. On service frontlines, AI is being used to streamline administrative processes, freeing up clinicians’ time for direct patient care. AI can also be used in diagnostics – for example in breast-cancer screening, where it can deliver performance comparable to expert radiologists.
Yet these use cases represent only a fraction of what is possible. AI creates an opportunity not just to improve efficiency, but to redesign how care is delivered altogether. It has the potential to enable more personalised care pathways, better coordination across fragmented services and more intelligent targeting of public resources.
By 2030, one in six people globally will be aged 60 or over. Health-care workers will be dedicating an increasing proportion of their capacity to conditions that typically affect older people, all while trying to maintain a high level of care for people in other life stages. AI offers capabilities that will be vital to alleviating both pressures. In elder care, AI-enabled services can help people remain independent for as long as possible, closing the gap between lifespan and “healthspan”. For others, AI can help streamline routine care, reducing overall pressure on the system.
The Situation Today
TBI and Japan House London have been exploring how liberal democracies such as the UK and Japan can govern effectively in the age of AI in a series of panel discussions, and our most recent event examined the use of technology to meet the needs of ageing populations.
Both Japan and the UK are advancing the use of AI in their health and care systems.
Japan, as a society at the forefront of global demographic shifts, is already integrating some AI and robotic tools into care for the elderly in particular. For example, the AI-based voice-analysis tool ONSEI screens for cognitive decline in under a minute by detecting subtle changes in speech patterns, providing a rapid and non-invasive diagnostic pathway. In care settings, the AIREC (AI-driven Robot for Embrace and Care) humanoid robot has demonstrated the ability to safely assist with physically demanding tasks such as repositioning patients, supporting caregivers in a system facing acute workforce shortages. This success in robotics is rooted in investment – the government spent over $300 million funding research in the 2010s on robots to care for the elderly.
The UK, meanwhile, has strengths that will help it deploy AI in health and care services, such as its integrated health system and advanced AI research base. It is also beginning to put in place the regulatory infrastructure needed to support safe scaling. The Medicines and Healthcare products Regulatory Agency (MHRA), for example, is advancing its AI Airlock programme, which provides a controlled environment for testing AI-enabled medical technologies with regulatory oversight. Backed by £3.6 million in multi-year funding, this reflects a shift towards more iterative, pro-innovation regulation and sits within the government’s broader efforts to support regulatory experimentation. Together, these capabilities could help give innovators clearer and faster routes to deploy AI tools across the health system while maintaining high safety standards.
Early examples of AI usage are also emerging in the UK. Cera, a digital-first home-health-care company, is using AI to analyse patient data and predict health risks before they escalate to hospitalisation. For its users, this has reduced hospital visits from as many as eight per year to three or fewer per year – improving patient outcomes while also easing pressure on the NHS.
However, both countries face practical barriers to expanding innovation. In the UK, NHS fragmentation means AI tools that succeed in one trust are often difficult to integrate across wider systems due to inconsistent data infrastructure, procurement constraints and differing local capabilities. In Japan, although investment in care robotics has been substantial, deployment remains uneven across municipalities and providers, particularly in rural or resource-constrained settings. There are also human challenges. Health-care workers are unlikely to adopt tools that increase workflow complexity or appear to threaten professional autonomy, while patients may be reluctant to engage with AI-supported care where transparency, accountability and human oversight are unclear. Without stronger integration and trust, many promising technologies risk remaining isolated demonstrations rather than system-wide solutions.
The Path Forward
AI can help futureproof our health and care systems, especially in countries with ageing populations. The question isn’t whether to incorporate AI into health care, but how best to do so. Early evidence from Japan and the UK shows a holistic approach is required:
Anchor deployment in measurable system outcomes. AI adoption should be driven by specific pressures within health and care systems – reducing avoidable hospital admissions, shortening diagnostic timelines or extending independent living. For example, predictive tools that identify early deterioration are valuable not because of their technical sophistication, but because they can reduce emergency admissions and relieve pressure on acute services. Framing deployment around these outcomes ensures that AI improves system performance rather than adding complexity.
Build ecosystems that enable integration, not just innovation. The main barriers to impact are not a lack of AI solutions, but the difficulty of embedding them into existing systems. In the UK, this is reflected in fragmented data infrastructure and procurement constraints within the NHS. In Japan, the challenge lies in scaling solutions across a highly decentralised care system. Addressing these issues requires coordinated investment in interoperable data systems, adaptive regulatory frameworks and procurement models that allow proven tools to scale. Without this, even high-performing technologies will remain confined to pilots.
Define people-centred care in operational terms. For patients, the value of AI will be measured by tangible improvements in care experience: faster access to services, more time spent in good health and greater ability to live independently. For clinicians and caregivers, it means reduced administrative burden and better decision support. Technologies that align with these outcomes – augmenting rather than replacing human care – are more likely to gain public trust and achieve sustained adoption.
With strong planning and prioritisation, AI can be embedded into systems in ways that measurably improve outcomes for both elderly members of society and across health and care services more broadly. For the UK and Japan, success will depend on a continued advance from innovation towards integration – translating technological capability into systems that provide better care and help people remain healthy and independent for longer.
Editor’s note: This commentary draws on insights shared during a panel event held at Japan House London in February 2026, which explored AI applications in health for ageing populations. The discussion featured Dr Ned Naylor (TBI), Kengo Shibata (TBI), Barbara Ubaldi (TBI) and Dr Ben Maruthappu (founder and CEO of Cera). The analysis and conclusions presented are solely those of TBI, in line with our intellectual independence policy.