Hu, Jiaxi; Mougiakakou, Stavroula; Xue, Song; Afshar-Oromieh, Ali; Hautz, Wolf; Christe, Andreas; Sznitman, Raphael; Rominger, Axel; Ebner, Lukas; Shi, Kuangyu (2023). Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease. The European physical journal plus, 138(5), p. 391. Springer 10.1140/epjp/s13360-023-03745-4
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Medical imaging has been intensively employed in screening, diagnosis and monitoring during the COVID-19 pandemic. With the improvement of RT-PCR and rapid inspection technologies, the diagnostic references have shifted. Current recommendations tend to limit the application of medical imaging in the acute setting. Nevertheless, efficient and complementary values of medical imaging have been recognized at the beginning of the pandemic when facing unknown infectious diseases and a lack of sufficient diagnostic tools. Optimizing medical imaging for pandemics may still have encouraging implications for future public health, especially for long-lasting post-COVID-19 syndrome theranostics. A critical concern for the application of medical imaging is the increased radiation burden, particularly when medical imaging is used for screening and rapid containment purposes. Emerging artificial intelligence (AI) technology provides the opportunity to reduce the radiation burden while maintaining diagnostic quality. This review summarizes the current AI research on dose reduction for medical imaging, and the retrospective identification of their potential in COVID-19 may still have positive implications for future public health.
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