Prevention has long been the rallying cry of health and care policymakers and planners, yet the global burden of chronic disease has never been greater.
In 2025, one in nine of the world’s adults has diabetes – 90% caught in a rising tide of type 2 diabetes after decades of poor diet (“over malnutrition”), underactivity, and obesity.1 This failure of preventive healthcare contributes to roughly 640 million people worldwide living with cardiovascular disease.2 Beyond physical illness, mental health problems are also on the rise3 – harming physical health,4 social wellbeing and the ability to work. Increasing numbers of people live with two or more long-term conditions – especially older adults and communities facing socioeconomic adversity, where combinations of chronic conditions can appear 10-15 years earlier than in more affluent communities.5
This is not just a public health crisis; it’s also an economic one. When such combinations appear in working-age people, the financial pressure on society intensifies. In many countries, people are living longer but spending more of their life in ill health, taking more time off work and having fewer children. An older, less productive workforce – together with pressure on pension funds and social care – deepens the economic strain. In the UK, the Office for Budget Responsibility now emphasizes the need for improving public health and worker productivity to underpin the wider economy.
At the same time, treatments continue to advance – especially medicines discovery and development, aided by AI. Yet 30-50% medicines are not taken as prescribed – leading to substantial harm and waste.6 For long-term conditions such as type 2 diabetes, optimal outcomes depend on complementary interventions, for example in diet and physical activity. There is a profound need for technologies – such as conversational AI and biosensors – that can tap into the rhythms of our lives and support healthier habits, including optimal diet, exercise, sleep and medication use.
The HealthTech sector is booming, but consumer products for the “worried well” do not deliver the preventive healthcare society needs. The global market for healthcare wearables alone is due to reach $250 billion by 2030.7 Most of these devices – and the rich data they generate – do not interoperate with medical records,8 and even if they did, infrequent consultations between patients and clinicians cannot reshape the daily choices that prevent or reduce illness. Healthcare must refocus from sparse consultations to continuous, impactful conversations – advances in conversational and multi-agent AI are ripe for this shift.
A move toward patient-centered “conversational healthcare” could also better integrate care across conditions, improving safety, effectiveness and value for money. For example, common medications for serious mental illness cause obesity, high blood pressure, diabetes, kidney disease, heart attacks and strokes. Patients may see psychiatrists, cardiologists, diabetes and kidney specialists, and primary care physicians – yet still die, on average, 20 years earlier than the rest of society due to lack of day-to-day support for their mental and cardiometabolic health.9 Similarly, depression is common among patients with diabetes, chronic lung disease, and chronic kidney disease, affecting their use of medicines and self-care. There is a long-neglected need to refocus digital health innovation from “medicine as usual” toward a more integrative “health-avatar” approach where frequent patient interaction creates more timely, holistic and preventive insights.10
Recent advances in generative AI (GenAI) and large language models (LLMs) have spawned conversational agents that interact by voice in thousands of languages and dialects. These are rapidly becoming the standard interface for public access to services – for example across India’s banking sector. Voice interaction is a critical opportunity for healthcare because it can overcome literacy barriers – general, digital and health literacy alike – that often block vulnerable patients from using HealthTech designed around reading and typing.
Lowering these barriers is key to shifting health systems toward prevention as they face growing care needs, widening inequalities, and limited resources. Healthcare AI must be designed with equity in mind. The evolution of “read-type/text” into “listen-talk/voice” interaction promises greater accessibility, while multi-agent AI offers new ways to target health and social care resources to those individuals and families in greatest need, coordinating care across providers for earlier, more integrated, and preventive intervention.
We are at a tipping point where emerging AIs could turn the vision of learning health systems into reality.11 My colleagues and I at the University of Liverpool’s Civic Health Innovation Labs (CHIL), working with UK health systems and industry partners, are building a three-level learning system, with continuous feedback loops linking the patient, provider and population levels. The disruptive data and decisions in this model are expected to emerge from daily conversations between patients and AI.
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