Big data, artificial intelligence, and structured reporting

The past few years have seen a considerable rise in interest towards artificial intelligence and machine learning applications in radiology. However, in order for such systems to perform adequately, large amounts of training data are required. These data should ideally be standardised and of adequate quality to allow for further usage in training of artificial intelligence algorithms. Unfortunately, in many current clinical and radiological information technology ecosystems, access to relevant pieces of information is difficult. This is mostly because a significant portion of information is handled as a collection of narrative texts and interoperability is still lacking. This review aims at giving a brief overview on how structured reporting can help to facilitate research in artificial intelligence and the context of big data.

Abstract licence: CC BY

1. 31.

   ESR paper on structured reporting in radiology · 2018

30 more references, whose wording the paper’s licence keeps private.

• To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines) · 2021

• Machine learning applications in prostate cancer magnetic resonance imaging · 2019

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