Journal of Medical Imaging and Radiation Oncology. 25 June 2021.
CM Jones MBBS, FRCR, FRANZCR; QD Buchlak MD, MPsych, MIS; L Oakden-Rayner MBBS, FRANZCR; M Milne MS; J Seah MBBS; N Esmaili PhD, MBA; B Hachey PhD.
Despite its simple acquisition technique, the chest X-ray remains the most common first-line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X-ray interpretation. While promising, these machine learning algorithms have not provided comprehensive assessment of findings in an image and do not account for clinical history or other relevant clinical information. However, the rapid evolution in technology and evidence base for its use suggests that the next generation of comprehensive, well-tested machine learning algorithms will be a revolution akin to early advances in X-ray technology. Current use cases, strengths, limitations and applications of chest X-ray machine learning systems are discussed.
This is an open access article distributed in accordance with the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by/4.0/