Machine Learning Models for COVID-19 Not Yet Suitable for Clinical Use

A recent systematic review of a host of scientific manuscripts, conducted by investigators from the University of Cambridge, has found that machine learning models for detecting or diagnosing COVID-19 are not yet suitable compared to standard medical imaging. The research was published in the journal Nature Machine Intelligence. "In the early days of the pandemic, there was such a hunger for information, and some publications were no doubt rushed," James Rudd, a co-author on the study said. "But if you're basing your model on data from a single hospital, it might not work on data from a hospital in the next town over: the data needs to be diverse and ideally international, or else you're setting your machine learning model up to fail when it's tested more widely." For the review, the investigators identified over 2,000 studies published between January and October of 2020, that claimed an ability to diagnose or prognosticate for COVID-19 from chest radiographs (CXR) and computed tomography (CT) images.