Across the world, populations are aging rapidly as people live longer and fertility rates continue to decline. Asia is at the vanguard of this demographic shift. The number of older adults (aged 60 and above) in the region is projected to triple between 2010 and 2050, reaching nearly 1.3 billion people. As Asian economies face this “silver wave,” helping older adults live safely and independently at home – a concept known as aging in place – has become a policy imperative.

At a recent webinar held during Stanford Health AI Week, the Asia Health Policy Program (AHPP) at Shorenstein APARC brought together experts from China, Singapore, and South Korea to share insights into the potential of health AI to allow older adults to enjoy healthy aging and avoid or postpone institutionalization. 

Moderated by Stanford health economist Karen Eggleston, the director of AHPP, the webinar featured Hongsoo Kim, a professor of health policy and aging at Seoul National University’s Graduate School of Public Health and director of its Artificial Intelligence Institute’s Center for AI in Health and Care; Xiaochen Ma, an assistant professor of health economics at Peking University’s China Center for Health Development Studies; and Tien Yin Wong, a physician-scientist-innovator and the senior vice-chancellor of Tsinghua Medicine and vice-provost of Tsinghua University, who has also worked and held senior leadership roles in Singapore and Australia as a practicing retinal specialist with a research portfolio on retinal diseases, ocular imaging, AI, and digital technology.

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Here are six lessons from the front lines of Asia’s efforts to integrate AI into elderly health care and advance aging in place:

1. Adopt a Whole Systems Approach

In South Korea, the world's fastest-ageing society, automated systems like "CLOVA CareCall" – an AI-powered well-being dialer – conduct natural-sounding check-ins with solo-dwelling seniors, boasting a 96% response rate. Yet, Professor Kim emphasizes that checking in with people in need of health care is only half the battle.

If an AI flags an isolated senior at risk of depression, cognitive decline, or a physical abnormality, but the local community lacks the social workers or clinical pathways to intervene, then the health care system has failed.

“The question is not only whether AI can detect something, but how a health and care system acts on it,” she says. “Detection by itself changes nothing. A warning that no one follows up on helps no one. So the gap I care about is not the model’s cleverness itself. It is whether the system delivers.”

2. Solve the Entire "Care Cascade"

In rural China, traditional diabetic screening rates hover below 33%, leaving millions at risk of Diabetic Retinopathy (DR), a leading cause of blindness. Professor Ma shared how deploying an AI screening model successfully pushed screening rates past 85%.

The research team, however, discovered a glaring bottleneck: only 21% of high-risk patients actually followed up to receive sight-saving treatments. To fill in this gap, Ma’s team designed an "AI Plus” model (v2.0) that integrates immediate, local-language counseling at the point of screening. To keep seniors healthy at home, AI solutions must address the entire clinical journey, from initial scan to final treatment.

“Many of the AI tools have been focused on diagnosis accuracy or validation rather than going downstream to the entire cascade of whether improved screening will transfer into improved referral and the ultimate health outcomes,” says Ma.

3. Align with Local Workflows and Incentives

AI and other technology solutions for health often fail because they expect overworked care workers to adopt entirely new habits. Professor Ma noted that digital health interventions in rural China succeeded only when they integrated seamlessly into existing daily routines.

Instead of forcing clinicians to use complex new software, successful pilots utilized WeChat, the ubiquitous messaging app already open on every phone. Furthermore, the technology must align with the financial and professional incentives of frontline health workers. If an AI tool increases their administrative burden without simplifying their day or boosting their clinical efficiency, then it will remain unused.

4. Design Human-Centered AI for Health Equity

Professor Wong highlighted the ethical risk that AI tools will worsen, rather than reduce, health care disparities. This challenge is driven by the dynamics of “Inverse Care Law,” where AI disproportionately benefits the already advantaged, and the “Recursive Care Law,” where this inequality becomes a self-reinforcing cycle embedded in the system.

Because younger, more tech-savvy individuals generate more health data, AI models become better at serving them than the intended users of aging-in-place technologies. This creates a vicious cycle where the very tools designed to support aging populations end up marginalizing them. Governments must devise policies to mandate fair data coverage and usability, ensuring that AI serves society's most vulnerable members equitably, Wong stated.

Professor Kim noted that her team found that only about 38% of community care agencies in Korea have adopted AI and that the adoption rate varied sharply by region. In fact, districts with the greatest need may have the least access to these powerful tools. This challenge is not a technology gap, Professor Kim argues, but a fundamental design gap. To be genuinely equitable, a system must be built from the start to actively track who is missing and automatically route support back to them. This requires two  human-centered design key principles:

I. Universal by Default: The hardest-to-reach should not have to be the most persistent in navigating the technology.

II. Connected Across Sectors: Long-term care, social care, and health care must act as one integrated system rather than disconnected silos, each of which sees only part of the person’s needs.

5. Augment, Do Not Replace, the Human Touch

The panelists rejected the trope of robots replacing human caregivers. Instead, they view AI as an essential force multiplier for an overstretched workforce.

Whether it is South Korea’s deployment of 12,000 AI companion robots to combat senior isolation, or automated triage tools in clinics, the goal should be to offload administrative and routine tasks. This frees up human social workers and clinicians to do what they do best: deliver hands-on, empathetic care.

6. Value Real-World Outcomes Over Technical Novelty

Healthcare systems should prioritize rigorous, real-world case studies that prove actual clinical value, such as reduced mortality, lower rates of blindness, or fewer nursing home admissions, rather than celebrating high validation benchmarks in a laboratory.

To build robust future health AI systems, the experts concluded, the academic and tech sectors must also courageously publish and analyze their failed trials to understand what truly works in the chaotic reality of home-based care.

While AI holds immense promise for helping people grow old at home, “age tech” alone cannot solve the elder care crisis, the panelists agreed.