How could AI help tackle healthcare’s demographic challenge?

Healthcare systems like the NHS are facing an increasingly difficult balancing act. Ageing populations, the growing prevalence of chronic conditions, and regional health inequalities are driving up demand and costs.

Without reform, these trends could threaten the long-term sustainability of healthcare services. Recent analysis shows that to keep health spending flat as a share of GDP, we need both significant productivity improvements and tangible gains in healthy life expectancy. Medical innovation, particularly artificial intelligence (AI), could play a meaningful role in that.

Where is AI making a difference today?

AI is already making its mark in healthcare, particularly in areas like diagnostics and imaging. In UK hospitals, for example, AI tools are now used to help interpret brain scans for stroke patients. AI’s ability to support administrative tasks is also advancing rapidly, with digital scribes being trialled to automate notetaking in consultations. These developments are helping clinicians to work more efficiently and focus on patient care.

Will AI replace doctors?

That’s not the goal. Most AI applications are designed to support or automate specific, well-defined tasks rather than replace clinical judgement. While some AI models have achieved impressive results in medical exams, fully automating patient consultations remains a long way off. The current focus is on augmenting human expertise, not replacing it.

What’s coming next?

Looking ahead, the biggest short-term gains will be seen in the back-office functions of healthcare systems: logistics, scheduling, record management, and patient communications. These are areas ripe for AI-driven improvement, offering quicker, more efficient services for patients and relieving pressure on clinical staff. While less glamorous than frontline clinical applications, improvements here could have a huge cumulative effect on healthcare productivity.

Further ahead, AI’s role in drug discovery and personalised medicine holds enormous potential. A milestone was reached recently when the first medicine designed using generative AI entered phase two trials – an early but exciting sign of what’s possible.

What are the risks of integrating AI into healthcare?

There are some ethical challenges. It’s vital that AI systems don’t inadvertently introduce bias or disadvantage certain groups. A well-known example is AI skin cancer diagnostic tools that historically performed less well on darker skin tones. Moreover, while AI can offer efficiencies, healthcare remains a deeply human experience. Ensuring people have the choice to speak with a human professional, especially when discussing sensitive issues, will be essential.

AI won’t solve the demographic pressures overnight, but it can be part of the solution – especially by improving productivity behind the scenes and making life easier for healthcare professionals. The challenge lies in implementation. We need to help health systems to see these technologies not as extra costs but as investments in long-term sustainability.