Born Jannis, Beymer David, Rajan Deepta, Coy Adam, Mukherjee Vandana V, Manica Matteo, Prasanna Prasanth, Ballah Deddeh, Guindy Michal, Shaham Dorith, Shah Pallav L, Karteris Emmanouil, Robertus Jan L, Gabrani Maria, Rosen-Zvi Michal
IBM Research Europe, Zurich, Switzerland.
Department for Biosystems Science & Engineering, ETH Zurich, Zurich, Switzerland.
Patterns (N Y). 2021 Jun 11;2(6):100269. doi: 10.1016/j.patter.2021.100269. Epub 2021 Apr 30.
Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance. Our analysis evidences a significant disparity between clinical and AI communities, in the focus on both imaging modalities (AI experts neglected CT and ultrasound, favoring X-ray) and performed tasks (71.9% of AI papers centered on diagnosis). The vast majority of manuscripts were found to be deficient regarding potential use in clinical practice, but 2.7% (n = 12) publications were assigned a high maturity level and are summarized in greater detail. We provide an itemized discussion of the challenges in developing clinically relevant AI solutions with recommendations and remedies.
尽管关于人工智能方法在新冠病毒医学成像方面的研究文章大量发表，但其临床价值仍不明确。我们对有关人工智能在新冠病毒患者护理成像中的效用的文献进行了最大规模的系统综述。通过在2020年全年在PubMed和预印本服务器上进行关键词搜索，我们识别出463篇手稿，并进行了系统的荟萃分析以评估其技术优点和临床相关性。我们的分析表明，临床和人工智能领域之间存在显著差异，在成像模态（人工智能专家忽视了CT和超声，更青睐X射线）和执行任务（71.9%的人工智能论文集中在诊断上）方面均如此。发现绝大多数手稿在临床实践中的潜在用途方面存在不足，但2.7%（n = 12）的出版物被评为高成熟度水平，并进行了更详细的总结。我们对开发具有临床相关性的人工智能解决方案所面临的挑战进行了逐条讨论，并给出了建议和补救措施。
