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AI boosts chest X-Ray reading in UK hospital study

NCT ID NCT06075836

Knowledge-focused Sponsor: Oxford University Hospitals NHS Trust Source: ClinicalTrials.gov ↗

What the study statuses mean

This study's is highlighted.

Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed This study
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Jun 26, 2026 · Last updated Jun 27, 2026 · Updated 1 time

Summary

This study tested whether an AI tool called Lunit INSIGHT CXR helps healthcare professionals read chest X-rays more accurately and quickly. Thirty doctors and radiographers reviewed 500 past chest X-rays from two UK hospitals, once with and once without AI help. The goal was to see if the AI improved their ability to spot problems like lung nodules, pneumonia, or collapsed lungs.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

Active substance
Lunit INSIGHT CXR (AI software)
What this could lead to
If successful, this could show that AI assistance improves how doctors and other staff interpret chest X-rays, potentially leading to faster and more accurate diagnoses in emergency and hospital settings.
What could go wrong
This is a small, retrospective study with 30 readers and 500 X-rays, so results may not apply to all hospitals or real-time use. The AI tool may not improve accuracy for all conditions or users.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

33 people

The number who actually took part.

Started

Oct 2023

Finished

Jun 2025

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

General radiologists/radiographers/physicians reviewing chest X-rays as part of their routine clinical practice, currently working in the National Health Service (NHS).

Ages

Children (under 18), adults (18 to 64) and older adults (65 and over)

Sex

Anyone

Healthy volunteers

Accepted

You do not need to have the condition being studied to take part.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

Inclusion Criteria: * General radiologists/radiographers/physicians who review CXRs as part of their routine clinical practice Exclusion Criteria: * Thoracic radiologists * Non-radiology physicians with previous formal postgraduate CXR reporting training. * Non-radiology physicians with previous career in radiology, respiratory medicine or thoracic surgery to registrar or consultant level

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Conditions

The condition(s) this trial relates to.

Bronchiolitis Obliterans Syndrome calcinosis Cardiomegaly middle lobe syndrome Multiple Pulmonary Nodules Pleural Effusion Pneumoperitoneum pneumothorax Pulmonary Atelectasis pulmonary coin lesion pulmonary fibrosis

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Atelectasis Cardiomegaly Fibrosis lung Pleural effusion Pleural effusions, chronic Pneumoperitoneum Pneumothorax Pneumothorax; acute Pulmonary calcification Pulmonary consolidation Pulmonary nodules, multiple Pulmonary nodules, solitary

Contacts and locations

Locations

  • Oxford University Hospitals NHS Foundation Trust

    Oxford, Oxfordshire, OX3 9DU, United Kingdom

More trials for these conditions

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