Healthcare has spent decades becoming better at treating disease. I want the next decade to be defined by preventing it, and AI is going to play a key role. This is because AI can bring foresight. Rather than waiting for patients to deteriorate before intervening, AI is beginning to identify subtle warning signs days in advance. Clinicians could intervene earlier, reducing hospital admissions, improving quality of life and easing pressure on overstretched healthcare systems. Combined with wearable devices that continuously monitor our health, this could fundamentally change how chronic disease is managed.
Integrating AI in healthcare
Continuous health monitoring has existed for years, but collecting data is only half the challenge. Traditional machine learning in healthcare has suffered from a variety of constraining factors – issues of variation in scale or data type consolidation, locking in a model that cannot generalise further to be more useful – which are now becoming easier to address with recent advances.
LLMs, in particular, have become multimodal to some extent (though, of course, trained in the same way as machine learning models). Even more innovative approaches take generalisation to the next step, creating reasoning frameworks that sit independently of the LLMs, meaning LLMs simply perform word generation rather than intelligence. The real breakthrough, however, is AI’s ability to simultaneously interpret enormous volumes of physiological, behavioural and environmental data, thereby enabling action. Rather than looking at a single measurement in isolation, modern AI models can identify subtle combinations of signals that humans would struggle to detect, revealing patterns that often precede clinical deterioration.
How do we give patients and healthcare systems time to act? Doing this – and doing it well through wearable technology – will enable us to anticipate deterioration before it becomes a crisis.
Needless to say, technology has evolved extremely quickly in the last few months, never mind the last few years. Wearables and AI are just two examples of rapidly evolving innovations that have moved far beyond simple fitness trackers and chatbots, respectively. Combining the two technologies could lead to an unparalleled transformation in how healthcare is delivered. If healthcare systems can move from reacting to disease to predicting it, the impact on patient outcomes, clinical workloads, and healthcare costs could be transformative.
A changing climate demands action
One of the clearest opportunities lies in chronic respiratory disease (CRD), which affects more than 545 million people worldwide. Asthma and chronic obstructive pulmonary disease (COPD) place an enormous burden on both patients and healthcare systems, with exacerbations frequently leading to emergency admissions that could potentially be avoided through earlier intervention.
Climate change makes predictive healthcare even more urgent. Poor air quality, rising pollen levels, heatwaves and wildfires are increasing respiratory risks worldwide. According to the World Health Organisation (WHO), 99% of the world’s population breathes air that exceeds recommended pollution limits, while UNICEF warns that over one billion children are exposed to multiple climate hazards. As environmental pressures intensify, healthcare systems will increasingly need tools that can anticipate deterioration rather than simply respond to it.
Predictive healthcare in practice
For CRD, predictive healthcare means integrating physiological indicators such as respiratory rate, heart rate, and wheeze detection with patient-reported symptoms and behavioural factors. Together, this multimodal data can reveal patterns that signal an increased risk of an exacerbation in the coming days.[Text Wrapping Break][Text Wrapping Break]For respiratory disease, that might include breathing rate, heart rate, sleep quality, physical activity, environmental conditions such as pollen counts or air pollution, alongside patient-reported symptoms. Viewed independently, each signal may reveal very little. When analysed together using AI, they can reveal patterns indicating an increased likelihood of an episode long before symptoms become severe.[Text Wrapping Break][Text Wrapping Break]This multimodal approach is one of AI’s greatest strengths. It allows algorithms to identify relationships across complex datasets that would be difficult, if not impossible, to recognisethrough traditional analysis.
Wearable devices, remote monitoring technologies, patient apps, diagnostic imaging, and laboratory tests generate vast amounts of information every day. Yet much of this data remains isolated in silos. Harnessing the wealth of healthcare data is how we reduce patient suffering, save lives and alleviate the pressure on overwhelmed healthcare systems. A warning delivered after a patient is already struggling to breathe is less valuable than one delivered 24, 48, or 72 hours earlier, which creates an opportunity for action.
Buying time
The opportunity is significant, and I’m not talking about using predictive AI to replace clinicians or automate healthcare decisions. Clinical judgement is still essential, and predictive systems must be rigorously tested and validated before they become part of routine care. Success will depend, perhaps most importantly, on patient trust.
If we can successfully deploy predictive AI for asthma and CRD, we could establish a blueprint for managing countless other chronic diseases. Effectively demonstrating how AI-powered wearables can provide early warning signals, support timely intervention, and ultimately transform healthcare from a reactive system into a predictive one will save lives, money, and time.
