Can open-source AI algorithms help clinical deployment?

A group in the U.K. has developed neural networks for interpreting chest x-rays that could help speed the adoption of AI systems in clinical settings, according to a study published December 8 in The Lancet Digital Health. In a retrospective cohort study, the researchers developed X-Raydar-NLP (natural language processing) and X-Raydar, deep neural networks that can accurately classify 37 common chest x-ray findings from images and their free-text reports. Moreover, they have made the neural networks available to others.“By making our model freely available, we hope to accelerate the adoption of automated systems in clinical settings to help relieve acute pressures on healthcare systems in the post-COVID-19 environment,” wrote co-first authors Yashin Dicente Cid, PhD, and PhD student Matthew Macpherson, of the University of Warwick.AI systems for automated chest x-ray interpretation that perform at close to human expert levels are now a reality and models that are trained on large data sets can overcome generalization concerns, which have held back deployment, according to the authors.Yet there are few freely accessible AI systems trained on large data sets for practitioners to use with their own data with the goal of accelerating deployment, they noted.To that end, the group first compiled the largest chest x-ray data set to date in the U.K. Six hospitals from three National Health Service (NHS) Trusts contributed 2.5 million chest x-ray studies from a 13-year period (2...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Subspecialties Chest Radiology Source Type: news