Vega Carlos
Luxembourg Centre for Systems Biomedicine, Bioinformatics Core GroupUniversité du Luxembourg 4365 Esch-sur-Alzette Luxembourg.
IEEE Access. 2021 Jul 6;9:97243-97250. doi: 10.1109/ACCESS.2021.3095222. eCollection 2021.
Advances in computer science have transformed the way artificial intelligence is employed in academia, with Machine Learning (ML) methods easily available to researchers from diverse areas thanks to intuitive frameworks that yield extraordinary results. Notwithstanding, current trends in the mainstream ML community tend to emphasise over knowledge, putting the scientific method aside, and focusing on maximising metrics of interest. Methodological flaws lead to poor justification of method choice, which in turn leads to disregard the limitations of the methods employed, ultimately putting at risk the translation of solutions into real-world clinical settings. This work exemplifies the impact of the problem of induction in medical research, studying the methodological issues of recent solutions for computer-aided diagnosis of COVID-19 from chest X-Ray images.
计算机科学的进步改变了人工智能在学术界的应用方式，得益于直观的框架，机器学习（ML）方法可供来自不同领域的研究人员轻松使用，这些框架能产生非凡的成果。尽管如此，主流机器学习社区的当前趋势往往强调数据而忽视知识，将科学方法搁置一旁，专注于最大化感兴趣的指标。方法上的缺陷导致方法选择的理由不充分，进而导致忽视所采用方法的局限性，最终使解决方案转化为实际临床应用面临风险。这项工作例证了归纳问题在医学研究中的影响，研究了近期从胸部X光图像进行COVID-19计算机辅助诊断解决方案的方法学问题。
