
Past performance does not predict future returns. You may get back less than you originally invested. Reference to specific securities is not intended as a recommendation to purchase or sell any investment.
This article is featured in the Q1 2026 Future Strategist newsletter, you can read the rest of the newsletter here.
For decades, radiology was a literal black box. Think of a darkened room where a specialist spent hours scrolling through grey scale images, hunting for subtle differences where a missed detail could change a life. Several waves of technology are rapidly turning this scene into a relic.
The long march to digital imaging
The digitisation of radiography did not happen overnight. It began quietly in the early 1970s, with a breakthrough that would reshape modern medicine: the first CT scanner. Instead of producing a single flat X-ray image, CT converted physical X-ray measurements into digital data, allowing computers to reconstruct cross-sectional slices of the human body. For the first time, doctors could see inside the skull or chest in three dimensions.
This laid the foundation for everything that followed. Over the subsequent decades, CT and later MRI scans became faster to produce and dramatically better in image quality. But there was a catch. As scanners improved, the number of images produced per scan exploded. What had once been a handful of pictures became hundreds, then thousands, per patient. Radiography became digital, but interpretation remained human.
When progress created a bottleneck
By the late 2000s, imaging was fully digital: scans were streamed across hospital networks instead of being stored on film. But success created a new constraint – volume.
Modern CT and MRI generate vastly more data, and demand has risen with ageing populations and earlier diagnosis. Images can be produced faster than they can be read. Radiologist capacity became the system bottleneck.

This strain is visible on both sides of the Atlantic. The NHS recently reported nearly half a million patients waiting more than six weeks for key diagnostic tests, driven less by scanner availability than by shortages of radiographers and radiologists. In the US, workforce reports cite vacancy rates for radiologic technologists of around 20%, with CT and MRI particularly affected. This means expensive machines can sit idle for lack of staff to run them or interpret results.
This is where AI entered radiology, not as a sci-fi replacement for doctors, but as a response to a system under pressure.
New hardware, smarter software
Recent advances are changing radiography in two ways: how images are captured and how they are processed.
A major hardware shift is photon counting CT. Traditional CT measures total X-ray energy, while photon counting CT registers individual photons, producing sharper images and better tissue contrast, and, in many protocols, enabling lower radiation doses.
Less visible but equally important is AI-powered reconstruction. By filling gaps in partially captured data, some MRI protocols can run up to 50% faster without sacrificing image quality. This means more patients per day on the same equipment, reducing waits and improving the economics of diagnostic centres.
The most dramatic change, though, is how scans are read. Traditionally, radiologists read studies in arrival order, so an urgent brain bleed could sit behind routine cases. AI changes this by screening images in the background as soon as they are captured. If it detects patterns consistent with stroke, pulmonary embolism or a collapsed lung, it flags the case and pushes it to the top of the worklist. This is prioritisation, not replacement: a tireless second set of eyes that helps catch time-critical findings when workloads peak.
The big question: are radiologists becoming obsolete?
This is the fear that often dominates headlines, the so-called Kodak moment. If AI can find tumours or bleeds, do we still need human radiologists? In 2026, the answer is still yes, but with an important caveat – the job is changing.
The narrative of replacement misses a simple reality: data volume is exploding faster than the workforce can grow. Even if AI reduces the time needed to read each scan, the number and complexity of scans is rising faster still. Without AI, the system would buckle. Instead of replacing radiologists, AI is removing drudgery. Routine normal scans can be filtered out, and reports can be drafted automatically. Radiologists increasingly act as editors, decision makers and clinical partners rather than typists and image scrollers.
Freed from repetitive tasks, radiologists are moving into higher value roles. Many now perform minimally invasive procedures using real-time imaging guidance or collaborate closely with oncologists and surgeons to tailor treatment plans. The job is shifting from passive image reading to active clinical decision-making.
What this means going forward
AI is not a threat to radiology; it is a pressure valve. It helps healthcare systems cope with staff shortages, rising demand and increasingly complex data. Going forward, its most practical value will be in workflow triage and safety netting: flagging time-critical findings early, reducing the risk that urgent cases sit unnoticed in a queue and creating a consistent audit trail of what was detected and when.
The real transformation is not machines replacing people but machines increasing clinical capacity and reliability so radiologists can spend more time on judgement, communication and patient care.
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KEY RISKS
Past performance does not predict future returns. You may get back less than you originally invested.
We recommend this fund is held long term (minimum period of 5 years). We recommend that you hold this fund as part of a diversified portfolio of investments.
The Funds managed by the Global Equities Team:
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