The UK’s first comprehensive evaluation of the use of Artificial Intelligence (AI) in breast cancer screening found that it can increase breast cancer detection by 10.4% and has the potential to reduce the workload of healthcare workers by more than 30% compared to the current clinical process.

Published today in Nature Cancer, the evaluation was carried out by a team of scientists, clinicians and software developers from the University of Aberdeen, NHS Grampian, and Kheiron Medical Technologies, now part of DeepHealth Inc., and was funded through the NHS AI in Health and Care Award in partnership with the National Institute for Health and Care Research (NIHR).

The study found that not only did AI help detect more cancers, most of which were invasive and high grade, it could also reduce the time to notify affected women from 14 days to just 3 days.This, the authors say, is ‘hugely significant given that the earlier detection of primarily high-grade cancers enables earlier treatment, which has a greater likelihood of treatment success.’

The researchers also found that using AI as part of the large-scale screening programme could reduce the number of women recalled unnecessarily for further assessment including unnecessary biopsies. This, the team say will significantly reduce patient stress and worry while also saving healthcare resources and costs.

The evaluation, led by the University of Aberdeen, followed NHS Grampian’s GEMINI (Grampian’s Evaluation of Mia in an Innovative National breast screening Initiative) project which was facilitated by the North of Scotland NHS Innovation Hub.

The team assessed how an AI software tool, Mia, developed by Kheiron, could be used to support healthcare workers in the routine breast screening of 10,889 women in NHS Grampian.

Dr Clarisse de Vries, Lecturer in Data Science at the University of Glasgow, lead author and former Research Fellow at the University of Aberdeen, explains why this GEMINI evaluation is so impactful for thousands of women:

“As part of the UK breast screening programme all women aged between 50 and 70 years old in the UK are invited for mammograms every three years. This results in over 2 million mammogram examinations being performed annually.

“Currently, in the UK, to reduce the number of cancers missed, two radiologists read every mammogram. However, some breast cancers are extremely hard to detect, and it is not always clear from mammograms whether breast cancer is present. So, when there is the suspicion of cancer on a mammogram the woman is recalled for additional investigations.

“Despite this, approximately 20% of cancers are missed using this process.

“Furthermore, many more women are recalled for further assessments than are diagnosed with cancer. For each five women recalled, approximately one will be diagnosed with breast cancer. So, they have had unnecessary, often invasive tests – not to mention the additional worry for the patient.

“This is why our findings are so important – not only did we find optimal ways to detect breast cancer, quicker and more accurately, we also found ways to reduce the number of women having to return for unnecessary tests.”

Niccolo Stefani, MD, Business and Product Leader Population Health & Clinical AI, DeepHealth added: “This study demonstrates how AI can do more than enhance clinical accuracy, it can reimagine how we deliver care.

“By detecting more cancers at an earlier stage, and reducing unnecessary interventions, we’re not only helping to improve outcomes for women today but also setting a new standard for scalable, proactive care. It’s a real-world example of how AI-powered solutions can potentially stage shift disease.”

The team found that AI could support breast screening by performing tasks similar to those that human experts perform, such as examining mammograms and highlighting potential areas of concern.

To comprehensively evaluate the different ways in which AI could support breast screening, seventeen different scenarios were tested by incorporating AI into the existing breast screening workflow at various points and with different operating point configurations.

The results showed that combining AI as a second reader – substituting one human reader, and as an extra reader serving as a safeguard, resulted in the best combination of workload savings and increased early cancer detection without recalling more women for additional tests.

Dr Clarisse de Vries said: “Healthcare and radiology are facing substantial challenges due to high workload, a shortfall of clinical radiologists, and an ageing population.

“However, despite the promise of AI, the UK National Screening Committee does not recommend the use of AI in the NHS breast screening programme. They previously highlighted that both the quality and quantity of the evidence base were insufficient. Our work adds high-quality evidence to the scientific literature in support of AI. It also demonstrates that AI use can be tailored to local healthcare needs to enhance service delivery.”

Dr Annie Ng, Science Lead at DeepHealth explains, “We are incredibly proud to show the real-world impact of clinical AI solutions for large scale screening to improve patient experience and outcomes. Programs like GEMINI are meaningful to build trust and accelerate AI adoption.”

Professor Lesley Anderson, Interdisciplinary Institute Director – Health, Nutrition and Wellbeing and Chair in Health Data Science at the University of Aberdeen explains how they did it: “Our unique trial design lets us simulate real-world use of AI in multiple ways, something never done before in this field.

“This pioneering approach allows healthcare service providers and policymakers to understand better how AI could be operationally integrated into clinical workflows to support breast screening and provide services with different options depending on their needs.”

Professor Gerald Lip, Clinical Director for breast screening in the North East of Scotland in NHS Grampian and Lead for Artificial Intelligence in Clinical Practice at the University of Aberdeen added: “Our results show that AI could effectively support breast screening services by increasing cancer detection and reducing doctors’ workload.

“Ultimately, for radiologists, AI augments practice.  Along with picking up more cancers, in UK and European screening programs where mammograms are read by two humans, partial substitution of one of the human readers for normal examinations can deliver real workload savings and reduce burnout. The bottom line here is – without AI, doctors would not have caught these cancers as early.

“The translation of AI into clinical practice is one of the operational challenges in the coming decade. Our findings and the novel way we have conducted this prospective study will inform the conversation around using AI in healthcare.”

The study’s findings help address several of the evidence gaps identified by the UK National Screening Committee. While further research is needed to fully quantify the benefits and any potential harms, this work provides an important foundation for next steps in the field. It directly supports the upcoming EDITH trial, which will expand this work to evaluate the use of AI in breast screening across sites throughout the UK. The Scottish element of the trial will be led jointly by the University of Aberdeen, NHS Grampian and University of Glasgow.

Professor Mike Lewis, NIHR Scientific Director for Innovation, said: “By generating high-quality evidence on the safe and effective use of AI in breast cancer screening, the team has shown its potential to improve detection, reduce unnecessary stress for patients, and ease pressure on the NHS workforce. The NIHR is proud to have funded this work, helping to ensure that cutting-edge technologies are tested rigorously and can be translated into real-world benefits for patients. This is exactly the kind of innovation we want to see delivering tangible improvements across the health system.”

Professor Gerald Lip received funding support through the Scottish Government’s Chief Scientist Office Innovation Fellowship Programme.

Case study – Yvonne Cook

In May 2023, Yvonne Cook from Aberdeen attended what she expected to be a routine mammogram appointment. In the waiting room she noticed a sign explaining that a project was underway involving artificial intelligence (AI) to assist in reviewing mammograms. Participation was optional.

“It didn’t occur to me for a minute to opt out,” she said. “I think it said that AI would be utilised as part of the research project to review the mammogram and I just thought, why not?”

A short time later, Yvonne –  in her 60s – received a recall letter requesting additional imaging. The wording was intentionally gentle.

“I guess they don’t want to alarm people unnecessarily, the letter said they wanted to do a follow-up mammogram which might be as a result of the initial result not being particularly clear.”

At the clinic, she learned the real reason for the recall: “When I arrived for that appointment, they said that it was the AI part of the analysis that had picked up something. I had a  scan and the consultant confirmed that the AI diagnosis was correct, that there was a small, Grade 2 tumour there, too small to be detected by the human eye.”

Yvonne had lobular breast cancer, the second most common type. Her emotional response to the diagnosis surprised even her: “You would expect a negative and emotional reaction to, ‘Oh my goodness, I have cancer,’ but overwhelmingly I just felt incredibly lucky that I was part of the research programme and that it had been picked up at this early stage.

“The fact that it started with me feeling incredibly lucky I think really helped to shape my positive approach to the treatment and everything that followed.”

Yvonne was immediately put on oestrogen-blocking medication to inhibit the growth of the tumour, which doctors determined that, while small, was aggressive.

Surgery took place in December 2023, followed by a second operation to remove more breast tissue in January 2024 to achieve clearer margins. Yvonne completed her treatment with a week of low dose radiotherapy in May 2024.

By the end of the treatment, it had become clear to her how pivotal AI had been: “Had the AI not picked up the small tumour when it did, then either it would have been discovered at my next routine mammogram three years later, or I would have picked it up when it had grown to a stage that I was able to feel it,” she said.

“If that had been the scenario, then it’s likely that the surgery would have been more invasive. The cancer could have spread, it could have involved chemotherapy and a much longer recovery time with more impact on my life.

“As it was, beyond some initial joint and muscle pain I experienced as a result of the oestrogen blocker, the impact on me wasn’t as significant as I feared. I was able to keep working throughout all of my treatment.”

AI has also reshaped Yvonne’s expectations around screening: “I do have faith in the current screening system but at the back of my mind when I go in for my checkups, I am thinking if there’s no AI as a second check, what if there’s something small there that isn’t picked up early?

“There could be many other women who would be in the same situation as me. Being caught very early through AI and dealt with quickly and in a less invasive way is a huge bonus.

“The flip side is that if the cancer isn’t detected, the treatment protocol could be quite different – not just the impact on my life, but also the cost to the NHS of treating a tumour that is much further advanced.

“Given it also helps reduce the number of women being recalled for second screenings, there is a significant benefit in terms of reducing stress and worry for women who don’t require treatment.”

Yvonne, the former Head of Tourism Development at VisitAberdeenshire, hasn’t let the experience hold her back. She continues to work part-time on a freelance basis as a tourism consultant/project manager, coordinating the Cruise Welcome Volunteers in Aberdeen, working as a guide and acting as Secretary of the Aberdeen City & Shire Hotels Association.

“I just feel incredibly lucky,” she added. “Lucky that AI was used, and lucky that it caught something so small at exactly the right time.”