AI는 암 발견률을 10% 높이고 의료 업무량을 30% 줄입니다.

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 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 nee