Artificial Intelligence and Infectious Disease Imaging

传染病(医学专业) 大流行 催交 数据科学 2019年冠状病毒病(COVID-19) 计算机科学 疾病 人工智能 人工智能应用 医学 工程类 病理 系统工程
作者
Winston T. Chu,Syed M. S. Reza,James Anibal,Adam J. Landa,Ian Crozier,Ulaş Bağcı,Bradford J. Wood,Jeffrey Solomon
出处
期刊:The Journal of Infectious Diseases [Oxford University Press]
卷期号:228 (Supplement_4): S322-S336 被引量:10
标识
DOI:10.1093/infdis/jiad158
摘要

Abstract The mass production of the graphics processing unit and the coronavirus disease 2019 (COVID-19) pandemic have provided the means and the motivation, respectively, for rapid developments in artificial intelligence (AI) and medical imaging techniques. This has led to new opportunities to improve patient care but also new challenges that must be overcome before these techniques are put into practice. In particular, early AI models reported high performances but failed to perform as well on new data. However, these mistakes motivated further innovation focused on developing models that were not only accurate but also stable and generalizable to new data. The recent developments in AI in response to the COVID-19 pandemic will reap future dividends by facilitating, expediting, and informing other medical AI applications and educating the broad academic audience on the topic. Furthermore, AI research on imaging animal models of infectious diseases offers a unique problem space that can fill in evidence gaps that exist in clinical infectious disease research. Here, we aim to provide a focused assessment of the AI techniques leveraged in the infectious disease imaging research space, highlight the unique challenges, and discuss burgeoning solutions.
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