From pixels to speech: The future of Radiology AI

TL;DR

• 70% of NHS and two thirds of US radiology departments now use AI tools

• Imaging AI leads the field, driving up to 791% ROI across hospitals

• Reporting AI improves efficiency by 15.5% while maintaining accuracy

• Voice AI scribes cut admin time by 30–40% and ease radiologist burnout

Radiology has long been the proving ground for medical AI.

Unlike many specialties, it digitized early and has since become somewhat of a bellwether for how new tools take hold in clinical practice.

Now it’s AI’s turn.

What was once confined to pilot studies is moving into daily practice, reshaping not just how radiologists read scans, but how they capture and share their insights.

Voice sits at the center of this shift.

Radiologists have dictated reports for decades, using speech recognition to navigate complex workflows.

Today, new forms of Voice AI are converging with imaging AI and generative reporting tools, creating a future where radiologists collaborate with machines across every stage of the diagnostic process.

That future is already taking shape. Two thirds of radiology departments in the US now use AI in some form, twice as many as in 2019.

In the UK, more than half of NHS radiology departments were already live with AI tools by 2023. This number has since risen to a 70% adoption rate. 

From imaging and reporting to scribes and staffing, this piece tracks how AI is transforming radiology, and asks where speech technology fits in.

Imaging: Radiology AI takes the first read

Imaging was always the obvious starting point for medical AI.

More than three quarters of FDA-cleared tools are built for imaging, designed to spot tumors, flag strokes or measure organ volume. 

That dominance reflects the specialty’s data-rich environment and its pressure points: growing scan volumes, rising complexity and not enough radiologists to meet demand.

Hospitals are turning to radiology AI tools because the returns are tangible.

In one stroke programme, an AI platform delivered a five-year ROI of 451%, the equivalent of $4.50 back for every $1 invested.

When radiologist time savings were included, the ROI jumped to 791%.

The pay-off for hospitals

Faster triaging, quicker reads and shorter reporting times not only free up clinical capacity but also speed treatment for patients, which in cases such as stroke can be the difference between recovery and long-term disability.

Yet these benefits come with new challenges.

Imaging AI often generates preliminary findings rather than full reports, leaving radiologists to validate and complete the narrative.

That shifts their role from sole author to editor-in-chief, with accuracy and workflow integration becoming the decisive factors for adoption.

While imaging may have been the natural starting point, the next phase of transformation is in how those findings are written up and shared across the clinical system. 

The workflow below shows how imaging outputs feed into drafting, validation and final sign-off — and where voice sits without breaking the flow of work.

Reporting: Radiology AI beyond the image

If imaging AI accelerates how radiologists see, reporting AI is changing how they explain what they see. 

The radiology report is more than a note in the system.

It is the clinical record that oncologists, surgeons and GPs rely on to guide treatment, so both speed and accuracy matter.

To show how AI is shifting this balance, the table below contrasts the traditional, manual process of reporting with how radiology AI is reshaping each stage of the workflow.

This shift is subtle but profound: radiologists spend less time typing and more time interpreting, while AI handles the clerical drag.

Clinical pilots are already quantifying the effect.

In 2024, a study in a US academic health system showed that generative AI auto-drafting improved documentation efficiency by 15.5%.

The time saved was equal to 12 full radiologist shifts, and diagnostic accuracy remained the same.

Other hospitals have started using AI tools that draft sections of reports in seconds and flag common errors such as incorrect left–right labelling before the report is signed off.

Can your radiology AI tell its ‘PIRADS’ from ‘pirates’?

The benefits extend beyond speed. Consistency is a long-standing challenge in radiology, as different clinicians may describe the same finding in different ways.

AI-driven reporting brings standardized formats and more uniform language, reducing ambiguity and making records easier to interpret across teams.

But trust is brittle.

Radiology relies on specialist vocabulary, and even tiny mistakes break the spell. A prostate score like PIRADS misread as “pirates” is not just a typo, it is a flashing warning that the system does not truly grasp the domain. 

Reports are legal records, after all, and precision is non-negotiable. For radiologists, that means less time drafting from scratch and more time policing AI output; a role shift that becomes even sharper once voice enters the mix.

Medical scribes: voice in radiology AI workflows

One of the fastest-growing areas of radiology AI is not in imaging or structured reporting, but in the quieter space of documentation. Here, Ambient AI is beginning to take hold.

These systems run in the background, listening as radiologists dictate findings and automatically drafting the report without breaking the flow of work. 

The approach builds on a long tradition: radiologists have dictated into transcription systems for decades, but the process often meant correcting errors or waiting for typed notes to return. 

With ambient AI scribes, dictation happens naturally and the report is generated in real time, ready for review and sign-off.

Adoption has accelerated across US health systems. Kaiser Permanente says that between 65%-70% of its physicians now use an AI scribe. At UCSF the figure is 40%, at UC Davis 44%, and at Providence Health 26% in the first year of rollout.

The efficiency gains are significant.

A peer-reviewed study found radiologists completed reports 2.5 minutes faster per case when using AI-generated drafts. This may seem a small margin per report until you think of the hours reclaimed each week across a department.

At Mass General Brigham, physicians cut documentation time from 90mins per day to under 30mins, with 79% saying they could focus more on patients and 60% reporting the technology made them more likely to extend their careers.

Paperwork also contributes heavily to burnout, and surveys show that 67% of clinicians felt ambient AI reduced their risk of burnout by easing the clerical load.

For radiologists, being able to speak findings naturally while an AI scribe manages the admin burden is as much a psychological relief as a technical one.

Voice AI scribes are proving that automation in radiology should not be confined to pixels or numbers.

It is also about reshaping everyday tasks in ways that make the job more sustainable – an increasingly urgent priority against the backdrop of workforce shortages.

The workforce gap: why radiology AI matters

While ambient AI scribes are easing the paperwork burden, they cannot solve the most fundamental problem in radiology: there are not enough radiologists. 

The UK faces a shortfall of nearly 2,000 consultant radiologists (a gap of around 29%), according to the Royal College of Radiologists’ latest workforce census. Other countries report similar strains as demand for scans continues to rise.

The mismatch between supply and demand is growing sharper. A CT scan that once generated 64 images can now produce thousands, often reconstructed into detailed 3D models. 

Reviewing every slice is mandatory, yet governments often buy new scanners without funding the staff to interpret them. The result: a growing bottleneck.

Radiology AI is pitched as the fix.

Imaging tools promise faster triage, reporting systems cut drafting time, and voice scribes trim clerical load. Together they stretch capacity, but adoption is uneven. Integration costs are high, licensing fees are rising, and accuracy remains non-negotiable. A tool that misses clinical nuance will not be trusted, however efficient it seems.

AI is not there to replace radiologists, but to keep them from being overwhelmed. That may be the only way to sustain the service at all.

The future of voice-enabled radiology 

Radiology’s rapid adoption of AI is setting the pace for medicine. The specialty generates huge volumes of digital data, faces acute workforce shortages and operates under constant pressure to deliver earlier diagnosis. That makes it the ideal test bed for automation.

But the lesson is not that AI replaces radiologists. It changes the work itself, from manual reporting to managing and validating AI-assisted outputs. Hospitals gain measurable ROI, patients benefit from faster diagnosis, and radiologists reclaim time from clerical burden.

Speech has always been at the core of radiology workflows. Radiologists speak their findings, and those words become the medical record that guides treatment.

At Speechmatics, our focus is on making voice AI accurate, accessible and adaptable to clinical environments.

That means handling complex medical terminology, coping with background noise, and supporting multiple languages and accents. 

Accuracy is of course non-negotiable. Credibility comes only when tools understand the specialist vocabulary of radiology without fail.

The next phase of radiology AI will not just be about pixels on a screen. It will be about how voice — still the radiologist’s most natural tool — becomes the foundation for a new kind of workflow.

FAQs

How is AI being used in radiology?

AI is now part of radiology workflows at three levels. Imaging AI triages scans, highlights tumors and measures organ volume. Reporting AI drafts structured reports, flags errors such as left–right mismatches and brings more consistent language. Voice AI scribes capture findings in real time, cutting documentation time by 30%-40% and reducing burnout. Together, these tools help departments manage rising demand and speed up diagnosis.

Is radiology getting replaced by AI?

No. Radiologists are not being replaced but their role is changing. Instead of drafting every report manually, they now review, refine and sign off radiology AI-assisted outputs. In imaging, they validate findings. In reporting, they ensure accuracy. In documentation, they free themselves from clerical burden. The radiologist remains the final authority in every step.

Is radiology at risk of AI?

The real risk is not AI itself but the shortage of staff. The UK faces a shortfall of nearly 2,000 consultant radiologists, around 29% of the workforce. AI is being positioned as a way to close this gap. By handling triage, drafting and paperwork, it stretches existing capacity further and makes workloads more sustainable.

Is AI more accurate in radiology?

AI can improve accuracy in specific areas. Imaging platforms have delivered ROI of up to 791% while maintaining diagnostic standards. Reporting AI flags common mistakes and enforces consistent language. Voice AI scribes reduce human error in transcription. But accuracy remains context dependent. Radiologists provide the clinical judgment and nuance that machines cannot replace. The most effective results come from human expertise combined with AI speed and consistency.