Radiology AI: A promising use for deep learning

Deep learning algorithms such as CNNs are an important innovation in chest x-ray (CXR) analysis, by detecting a range of diseases that are evidence on CXR. By learning to recognise patterns from large, labelled datasets, they are now able to attain a high diagnostic accuracy.

The potential benefits are significant. Such algorithms can increase the performance of diagnosis, identifying diseases that might have been missed by a human, improving patient outcomes. In addition, by reducing the human image analysis workload, the hospital is more efficient. It might even expand access to high quality healthcare, such as areas that have fewer radiologists.

On the other hand, there are some concerns about implementing radiology AI systems. This includes data privacy and security concerns, as there is a lot of patient information requires. Moreover, there are concerns about algorithmic bias stemming from skewed training data. It is essential that the performance of the algorithm is good across all groups.