Literature DB >> 31582171

Artificial intelligence in clinical imaging: a health system approach.

F J Gilbert1, S W Smye2, C-B Schönlieb3.   

Abstract

The development and application of artificial intelligence (AI) to radiology requires an approach that encompasses a health system. The UK government and National Health Service (NHS) are creating an ecosystem to facilitate academic/industrial partnerships aimed at accelerating the creation of relevant and robust AI tools, which will improve the development and delivery of healthcare imaging. A series of recent initiatives are described, which will drive the development and adoption of AI in clinical imaging.
Copyright © 2019 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Mesh：

Year:  2019        PMID： 31582171     DOI： 10.1016/j.crad.2019.09.122

Source DB:  PubMed          Journal:  Clin Radiol        ISSN： 0009-9260            Impact factor:   2.350


  3 in total

1.  Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic.

Authors:  Dominic Cushnan; Rosalind Berka; Ottavia Bertolli; Peter Williams; Daniel Schofield; Indra Joshi; Alberto Favaro; Mark Halling-Brown; Gergely Imreh; Emily Jefferson; Neil J Sebire; Gerry Reilly; Jonathan C L Rodrigues; Graham Robinson; Susan Copley; Rizwan Malik; Claire Bloomfield; Fergus Gleeson; Moira Crotty; Erika Denton; Jeanette Dickson; Gary Leeming; Hayley E Hardwick; Kenneth Baillie; Peter Jm Openshaw; Malcolm G Semple; Caroline Rubin; Andy Howlett; Andrea G Rockall; Ayub Bhayat; Daniel Fascia; Cathie Sudlow; Joseph Jacob
Journal:  Digit Health       Date:  2021-11-23

Review 2.  Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations.

Authors:  Sarah E Hickman; Gabrielle C Baxter; Fiona J Gilbert
Journal:  Br J Cancer       Date:  2021-03-26       Impact factor: 7.640

3.  Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis.

Authors:  Robert J O'Shea; Amy Rose Sharkey; Gary J R Cook; Vicky Goh
Journal:  Eur Radiol       Date:  2021-04-16       Impact factor: 5.315
  3 in total

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