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		</div><p>Researchers have created an artificial intelligence (AI) system they claim can spot abnormalities in chest X-rays and speed up the processing of screenings.</p>
<p>The software uses computer vision to recognise radiological abnormalities in X-rays and then suggest how quickly these should be reported by a radiologist.</p>
<p>The research from the University of Warwick claims the system could help cut the average delay in receiving an expert opinion from 11 days to less than three.</p>
<p>The study, carried out by Warwick Manufacturing Group (WMG), an academic department of the university, also involved an algorithm that was capable of reading radiological reports, understand the findings and the priority level of the exam.</p>
<p>The research, which was carried out through work with Guy’s and St Thomas’ NHS Foundation Trust in London, has been published in the journal Radiology.</p>
<p>It used half a million anonymized chest X-rays to develop the AI system, which the developers said was able to learn the visual patterns in X-rays and link it to an urgency level.</p>
<p>Professor Giovanni Montana, leader of the research team and chairman in data science at the WMG at the University of Warwick, said: <em>“Artificial intelligence-led reporting of imaging could be a valuable tool to improve department workflow and workforce efficiency.</em></p>
<p><em>“The increasing clinical demands on radiology departments worldwide has challenged current service delivery models, particularly in publicly-funded healthcare systems.</em></p>
<p><em>“It is no longer feasible for many radiology departments with their current staffing level to report all acquired plain radiographs in a timely manner, leading to large backlogs of unreported studies.</em></p>
<p><em>“In the United Kingdom, it is estimated that at any time there are over 300,000 radiographs waiting over 30 days for reporting.</em></p>
<p><em>“The results of this research shows that alternative models of care, such as computer vision algorithms, could be used to greatly reduce delays in the process of identifying and acting on abnormal X-rays – particularly for chest radiographs which account for 40% of all diagnostic imaging performed worldwide.</em></p>
<p><em>“The application of these technologies also extends to many other imaging modalities including MRI and CT.”</em></p>
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