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.
This collection constitutes a compilation of selected texts, i.e., articles, manuscripts, and/or other research outputs, sharing a focus on issues in Radiology and Natural Language Processing (NLP). This scholarly and scientific anthology groups works that are renowned for their contribution and impact. Centrally differentiating this collection is its reliance on scientific and scholarly discernment, in epistemically selecting and listing research work products. Works undergo explicit incorporation based on the quality of their text, defined by prose clarity and an ability to influence readers' thoughts with minimal interruption, alongside the merit of the research on which they are based. Should you want to contribute to this collection, by adding published research or through planning for the promulgation of a work in progress, please reach out and mention your work.
|Main image credit:
Faugas, Woodger G. (2022). Radiology and Natural Language Processing Collection Main Image. ScienceOpen.
|Background image credit:
Faugas, Woodger G. (2022). Radiology and Natural Language Processing Collection Background Image. ScienceOpen.
|Applied linguistics, General medicine, Applied computer science, Radiology & Imaging, Artificial intelligence, Human biology