AI in diagnostic imaging

Hospitals across the United Kingdom face a record backlog. New figures from the Royal College of Radiologists (RCR) confirm that 976,000 scans in 2024 were not reported within the NHS 30-day target, a 28% rise on 2023.¹

The same analysis shows a 30% shortfall in consultant radiologists (4,923 in post versus 6,467 required) and an 8% increase in CT and MRI demand.¹ To cope, trusts spent £325 million on extra reporting capacity last year, including a record £216 million paid to private teleradiology firms, more than double the pre-pandemic level.²

“It is a false economy to be spending over £200m of NHS funds outsourcing radiology work to private companies, and evidence of our failure to train and retain the amount of NHS radiologists we need,” said Dr Katharine Halliday, President, RCR. “We must plan for the long-term, training the workforce we need to meet demand while embracing solutions that can boost our productivity so that patients no longer face such agonising waits for answers.”¹

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The human cost of delay

Every four-week delay in a verified report can postpone cancer staging, fracture repair, and stroke treatment. David Rowland of the Centre for Health and the Public Interest warns that heavy outsourcing “risks hollowing out NHS radiology departments” and deprives trainees of supervised learning.²

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Why AI in diagnostic imaging matters

AI in diagnostic imaging uses machine-learning algorithms to analyse medical images and flag abnormalities. In radiology it can:

Triage normal studies so radiologists focus on complex cases.
Improve sensitivity and specificity for subtle abnormalities.
Cut reporting time from minutes to seconds, easing workload.
Standardise care across sites with limited specialist cover.

These benefits echo the RCR’s call for “technology that will improve productivity” and align with the NHS Long Term Workforce Plan.

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NICE backs AI fracture detection

In January 2025 the National Institute for Health and Care Excellence (NICE) completed an Early Value Assessment (EVA) of fracture-detection tools. AZmed’s AZtrauma, part of the Rayvolve® suite, stood out: across 16 studies it lifted fracture-detection sensitivity from 86.5% to 95.5% when used as a second reader.³ NICE estimated an implementation cost of about £1 per X-ray, a fraction of today’s outsourcing fees.

Mark Chapman, Director of HealthTech, NICE, said, “These AI technologies are safe to use and could spot fractures which humans might miss given the pressure and demands these professional groups work under.”

NICE concluded that AI in imaging can reduce variability, speed diagnosis, and save money, provided real-world evidence continues to grow.

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Connecting the dots: AZmed and the NHS

AZmed, one of the leading AI diagnostic imaging companies, now supports more than 2,500 healthcare facilities in 55+ countries with its CE-marked and FDA-cleared AI tools.⁵ UK momentum accelerated when NICE listed AZtrauma (Rayvolve®) in the EVA and the NHS signalled plans to deploy the AI for frontline fracture care.⁴ For overstretched radiology teams this promises:

Faster clinical decisions in emergency departments.
Lower missed-fracture rates, boosting patient safety.
A shift in spending from reactive outsourcing to sustainable in-house AI.

By integrating AI in diagnostic imaging solutions such as AZmed’s Rayvolve®, hospitals can reclaim reporting time, reduce costs, and improve outcomes, the long-term remedy the RCR is calling for.

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Download the NICE one-pager

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