Jemioło Paweł, Storman Dawid, Orzechowski Patryk
AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, al. A. Mickiewicza 30, 30-059 Krakow, Poland.
Chair of Epidemiology and Preventive Medicine, Department of Hygiene and Dietetics, Jagiellonian University Medical College, ul. M. Kopernika 7, 31-034 Krakow, Poland.
J Clin Med. 2022 Apr 6;11(7):2054. doi: 10.3390/jcm11072054.
The COVID-19 pandemic has sparked a barrage of primary research and reviews. We investigated the publishing process, time and resource wasting, and assessed the methodological quality of the reviews on artificial intelligence techniques to diagnose COVID-19 in medical images. We searched nine databases from inception until 1 September 2020. Two independent reviewers did all steps of identification, extraction, and methodological credibility assessment of records. Out of 725 records, 22 reviews analysing 165 primary studies met the inclusion criteria. This review covers 174,277 participants in total, including 19,170 diagnosed with COVID-19. The methodological credibility of all eligible studies was rated as : 95% of papers had significant flaws in reporting quality. On average, 7.24 (range: 0-45) new papers were included in each subsequent review, and 14% of studies did not include any new paper into consideration. Almost three-quarters of the studies included less than 10% of available studies. More than half of the reviews did not comment on the previously published reviews at all. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Such information chaos is alarming. It is high time to draw conclusions from what we experienced and prepare for future pandemics.
新冠疫情引发了大量的初步研究和综述。我们调查了出版过程、时间和资源浪费情况，并评估了关于利用人工智能技术诊断医学图像中新冠病毒的综述的方法学质量。我们检索了9个数据库，检索时间从建库至2020年9月1日。两名独立评审员负责记录的识别、提取以及方法学可信度评估的所有步骤。在725条记录中，22篇分析165项初步研究的综述符合纳入标准。这篇综述总共涵盖174,277名参与者，其中19,170人被诊断为新冠患者。所有符合条件的研究的方法学可信度评级为：95%的论文在报告质量方面存在重大缺陷。平均而言，每篇后续综述纳入7.24篇（范围：0 - 45篇）新论文，14%的研究未考虑任何新论文。近四分之三的研究纳入的现有研究不到10%。超过一半的综述根本没有对之前发表的综述发表评论。如果参考之前的综述并遵循方法学指南，很多时间和资源的浪费是可以避免的。这种信息混乱令人担忧。是时候从我们的经历中吸取教训，为未来的大流行做好准备了。
