Reference : From Hume to Wuhan: An Epistemological Journey on the Problem of Induction in COVID-1...
Scientific journals : Article
Engineering, computing & technology : Computer science
From Hume to Wuhan: An Epistemological Journey on the Problem of Induction in COVID-19 Machine Learning Models and its Impact Upon Medical Research
Vega Moreno, Carlos Gonzalo mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core]
IEEE Access
Yes (verified by ORBilu)
[en] covid-19 ; Biomedical imaging ; philosophical considerations ; machine learning ; Computer aided diagnosis
[en] 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 <italic>wins</italic> 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.
Researchers ; Professionals

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