Machine learning in radiology targets efficiency

Machine learning in radiology targets efficiency

Written by Simon Harris for AuntMinnie Europe

Whilst artificial intelligence (AI) is unlikely to replace radiologists any time soon, a new breed of machine learning-based software applications is poised to take on many of their tedious, repetitive, and time-consuming tasks – improving productivity and freeing up more time to focus on value-added activities.

In most countries, radiologists are already operating at, or near, capacity; any further gains in efficiency is likely to be derived from the use of “intelligent” workflow software tools. Furthermore, radiology is evolving from a largely descriptive reporting model to a more quantitative discipline, placing added demands on radiologists. As a result, the need for workflow efficiency has never been greater. It’s time to cut through the AI hyperbole and take advantage of the many benefits that machine learning is bringing to radiology.

There is a growing array of intelligent image analysis products that automate various stages of the imaging diagnosis workflow. Whilst early generation computer-aided detection (CAD) products largely failed to meet expectations, the application of advanced machine-learning techniques such as deep learning will, in part, enable CAD products to evolve from purely detection systems to more advanced decision-support tools.

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