Machine Learning for COVID Diagnosis Falls Short Posted on : Mar 23 - 2021

In the earliest days of the pandemic, machine learning showed exceptional promise for COVID-19 diagnosis. Reliably, early machine learning models outperformed doctors in recognizing the telltale COVID-induced pneumonia on CT scans from hospitalized patients. However, more conventional testing methods quickly lapped machine learning-based methods, detecting the onset of COVID well before hospitalization and with greater accuracy. Now, a year later, a team of researchers led by the University of Cambridge has concluded a review of COVID diagnosis ML models, finding that even in 2021, none of the proposed models are suitable for clinical use.

The researchers whittled down 2,212 studies, eventually focusing on 62 studies – most of which were not peer-reviewed – published between January 1st and October 3rd of 2020, all of which presented machine learning models for diagnosing or predicting COVID-19 infection based on X-rays and/or CT scans. These 62 studies collectively described more than 300 such models – and the researchers found all of them substantially lacking.

 “The international machine learning community went to enormous efforts to tackle the COVID-19 pandemic using machine learning,” said James Rudd, one of the senior authors of the review and a member of Cambridge’s Department of Medicine. “These early studies show promise, but they suffer from a high prevalence of deficiencies in methodology and reporting, with none of the literature we reviewed reaching the threshold of robustness and reproducibility essential to support use in clinical practice.” View More