AI-driven technology does not speed up lung cancer diagnosis

Using artificial intelligence tools to read thousands of chest X-rays and prioritise abnormal ones did not lead to faster lung cancer diagnoses, according to a new study led by researchers from UCLH, UCL and the University of Nottingham.

The LungIMPACT trial, the largest randomised trial of its kind and led from UCLH, ran across five NHS trusts in England and found no significant reduction in the time from X-ray to cancer diagnosis when AI flagged abnormal scans so that a radiologist could prioritise these for early review. The study, published today in Nature Medicine, only tested the impact of AI prioritisation and did not change the usual care pathway.

AI prioritisation did speed up one part of the process: the median time for a radiologist to report on a chest X-ray fell from 47 hours to 34 hours. But this improvement did not cascade into faster progress through the rest of the diagnostic pathway. Referral rates, treatment start times, and cancer stage at diagnosis were all comparable across both groups.

UCLH consultant radiographer and UCL honorary associate professor Nick Woznitza, the principal investigator of the trial, said: “The hypothesis was that getting the X-ray reported faster would cascade into a faster diagnosis, but the NHS system simply could not respond at the same pace.

“The bottleneck isn’t the reporting; it’s everything that happens next: telling the patient, the CT appointment, the clinic slot, the multidisciplinary meeting. We’ve shown that AI prioritisation, by itself, cannot fix that,” Dr Woznitza added.

Some of the key findings of the trial include:

• 93,326 chest X-rays analysed across five NHS hospitals
• 558 patients diagnosed with lung cancer during the study
• Difference in median time to cancer diagnosis was 44 versus 46 days (not significant)
• 30% of X-rays showed a discrepancy between AI and radiologist findings
• 28,000 reviews of discrepancies conducted by expert radiologists and radiographers
• Median wait for diagnosis – when both AI and radiologist initially did not detect the cancer – was 177 days

Among the 558 patients diagnosed with lung cancer during the trial, the median time from chest X-ray to diagnosis was 44 days with AI prioritisation and 46 days without – a two-day difference that was deemed not significant. The median time from X-ray to the more detailed CT scan was identical in both groups at 53 days. The median time from chest X-ray to a fast-tracked CT scan (because cancer was suspected) was six days with AI prioritisation and seven days without, again not deemed significant.

A closer examination of patients ultimately diagnosed with cancer revealed a stark pattern. When both the AI and the radiologist judged an X-ray as abnormal, the median time to cancer diagnosis was 38 days. When both judged it as normal (X-rays detect only four out of five cancers), the median time stretched to 177 days, nearly five months.

There were 53 cases where the AI flagged the X-ray as abnormal, but the radiologist’s report did not identify an abnormality. These patients waited a median of 106 days for their cancer diagnosis, significantly longer than when both agreed. The researchers say this group warrants urgent further investigation, as some could have been diagnosed at an earlier stage.

UCLH consultant radiologist and UCL honorary associate professor Arjun Nair said: “The cases where the AI spotted something the radiologist did not flag are the ones that interest us most. These patients waited much longer for a diagnosis. We need to understand whether there is a pattern, as this has implications not just for how we use the AI to spot these cases earlier, but also potentially how we train radiologists and radiographers of the future.”

The NHS’s own National Optimal Lung Cancer Pathway (NOLCP) specifies that patients should progress from a suspicious chest X-ray to a CT scan within 72 hours – ideally on the same day. The trial found that of more than 93,000 X-rays analysed, only 172 CT scans were performed on the same day, and just 477 within the 72-hour window. The NOLCP standard is being routinely missed regardless of whether AI prioritisation is active.

The authors argue that what is needed is not AI prioritisation alone, but a redesign of the whole pathway so that when AI flags a suspicious X-ray, a coordinated series of actions (CT booking, clinical review, specialist referral) is automatically triggered before the patient leaves the department. This will take investment in infrastructure and workforce, not just AI, the study concludes.

University of Nottingham professor of medicine David Baldwin, who is also consultant respiratory physician at Nottingham University Hospitals NHS Trust and chief investigator of the study, said: “The technology is maturing, we need to understand how busy departments can benefit most given the challenges they face.

“And the pathway is not ready for this. If we want AI to make a real difference to lung cancer outcomes, we need to redesign how the NHS responds when the AI raises an alert, and that means better coordination, more resource, and a genuine commitment to same-day action,” Professor Baldwin added.

The trial was supported by the National Institute for Health and Care Research Biomedical Research Centre at UCLH.