Radiology and Natural Language Processing by Woodger Faugas

A multidisciplinary field referred to as Natural Language Processing (NLP) combines Computational Linguistics and Computer Science, especially via the coexisting, if subsidiary, domain of Artificial Intelligence (AI), in helping machines process and decipher human language for the purpose of achieving innumerable, useful applications. Deep Learning (DL) innovations made recently are starting to significantly enhance NLP-aided work performance. Fundamentally, as well as undoubtedly, these methods could help develop automated tools that would meaningfully enhance clinical decision-making through helping Radiologists and other clinicians access new insights derived from the mining, processing, and querying of unstructured text sourced from Radiology reports, and other clinical data. Such a development would, in turn, significantly help optimize the provision and delivery of personalized, or differentiated, care to patients in need. These applications must intrinsically draw upon evidence- and science-based usage of new and time-honored DL, NLP, and linguistic methods in helping to inform clinical decision-making in a priori as well as a posteriori fashions. Through anthologizing ground-breaking and impactful research within Radiology and NLP, this collection constitutes a dialogic conduit via which Radiologists, clinicians, and researchers can exchange ideas regarding fruitful emergences and developments in the field.