How AI is reshaping the future of healthcare [Q&A]

Artificial intelligence is reshaping healthcare systems across the globe. But to see where it stands today, and where it could be in the coming years, we need to better understand how AI can accelerate patient discharges, improve cancer detection, and support overstretched staff, while also addressing the barriers that have slowed adoption.

AI won’t replace doctors, but it can save healthcare. To find out how we spoke to prominent CIO with decades of healthcare experience, Richard Corbridge. Richard was previously the Chief Information Officer for the Health Service Executive in Ireland and was instrumental in the reform of the Irish healthcare technology system.

BN: Everyone is talking about AI. How do you see its future in healthcare, both here in the UK and internationally?

RC: AI isn’t the future, it’s already here. The real question is how quickly we can embed it into everyday practice. In the UK, the opportunity is enormous. We’re facing record waiting lists, chronic workforce shortages, and an ageing population. AI gives us tools to manage demand, support clinicians, and spot disease earlier than ever.

Internationally, countries are moving at different speeds. The US is pushing ahead in radiology and robotic surgery. China is deploying AI diagnostics at scale. And smaller nations like Singapore and Israel are moving quickly as their systems are centralised and agile. The UK has world-class research and a unique National Health Service (NHS), but we need to shift from pilots to permanent solutions if we want to keep pace.

BN: Hospitals are struggling with discharge delays. Can AI really make a difference there?

RC: Absolutely. Delayed discharges are one of the biggest bottlenecks in the system. Patients often stay longer than needed because of paperwork or coordination issues. AI can predict when someone will be ready to leave, automate discharge documentation, and connect hospital teams with social care providers.

The result is quicker discharges, more available beds, and less pressure on staff. it’s exactly the kind of practical problem where AI can deliver immediate impact.

BN: Cancer detection is often cited as a prime use case. How close are we to making that mainstream?

RC: Very close. AI is already matching -- and sometimes exceeding -- human radiologists in detecting cancers like breast and lung. But the real breakthrough is speed. Instead of waiting weeks, AI can flag suspicious scans in minutes, sending them straight to the top of the queue for review.

The real power lies in integration. Imagine a dashboard that combines imaging, pathology, genomics, and medical history into a single risk profile. That’s where AI moves from being a tool to being a genuine partner in care.

BN: Which countries are adopting AI the fastest?

RC: The US leads in investment and deployment. China is scaling rapidly. Singapore and Israel are leaders because they’ve built national strategies around AI in healthcare, backed by strong digital infrastructure. In Europe, Denmark and Estonia are well ahead thanks to their modern electronic health records and interoperability.

The UK has the research base and the data, but historically adoption has been slow because of legacy IT systems and fragmented procurement. If we’re serious about competing, we need to be braver in implementation.

BN: The NHS is under pressure with waiting lists and staff shortages. Can AI realistically help?

RC: Yes, but we must be realistic. AI won’t create more doctors and nurses. What it can do is help the workforce we have operate more effectively.

For waiting lists, AI can triage patients by urgency, optimise theatre schedules, and forecast demand. For workforce shortages, AI can take over routine admin -- writing notes, coding records, managing digital triage -- so staff spend more time on patient care.

Globally, we’re seeing similar approaches. In Japan, AI supports elder care. In Africa, AI chatbots and triage tools extend clinical reach into rural areas. Everywhere, the aim is the same: stretch limited resources further.

BN: How critical are cloud computing, interoperability, and data sharing in all of this?

RC: Absolutely fundamental. AI runs on data, and healthcare data has been stuck in silos for decades. Cloud computing gives us the scale and security to handle national datasets. Interoperability standards, like FHIR, mean different systems can talk to each other. And secure data-sharing platforms allow clinicians and researchers to collaborate while keeping patient privacy intact.

Without these foundations, AI projects remain pilots. With them, we can train models on millions of patient records, not just a handful. And that’s when AI really becomes powerful.

BN: People worry about privacy. How do you build trust?

RC: By being transparent. Patients need to know what data is being used, by whom, and for what. They need guarantees of security, and they need to see the benefits directly, whether that’s quicker diagnoses or less time repeating their history at every appointment.

If people feel their data is being exploited rather than used for public good, adoption will stall. The NHS has a huge reservoir of public trust, but it’s fragile. We need to earn it every day.

BN: What are the main barriers holding back adoption?

RC: Four things: outdated infrastructure, slow procurement processes, lack of digital skills in the workforce, and cultural resistance. None of these are insurmountable, but they require leadership and urgency. Other countries are moving fast. We can’t afford to spend another five years piloting technology we already know works.

BN: Fast forward 10 years -- what does success look like?

RC: Success is when AI becomes invisible. Patients won’t think, ‘This is AI.’ They’ll just experience smoother, faster, more personalised care. Clinicians won’t feel burdened by another system; they’ll feel supported by a digital colleague.

At the system level, it means shorter waiting lists, less staff burnout, and better outcomes. Globally, it means fairer access to quality care, even in low-resource settings.

The technology is ready. The real challenge is whether we have the will to use it at scale. AI isn’t a futuristic add-on; it’s a practical tool that could transform the NHS if deployed boldly and at scale. But without decisive leadership, modern infrastructure, and public trust, the UK risks falling behind international peers.

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