https://doi.org/10.4081/ecj.2020.8573
Deep learning in Emergency Medicine: Recent contributions and methodological challenges
Submitted: 23 September 2019
Accepted: 20 December 2019
Published: 17 March 2020
Accepted: 20 December 2019
Abstract Views: 1303
PDF: 465
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
In the last few years, artificial intelligence (AI) technology has grown dramatically impacting several fields of human knowledge and medicine in particular. Among other approaches, deep learning, which is a subset of AI based on specific computational models, such as deep convolutional neural networks and recurrent neural networks, has shown exceptional performance in images and signals processing. Accordingly, emergency medicine will benefit from the adoption of this technology. However, a particular attention should be devoted to the review of these papers in order to exclude overoptimistic results from clinically transferable ones. We presented a group of studies recently published on PubMed and selected by keywords ‘deep learning emergency medicine’ and ‘artificial intelligence emergency medicine’ with the aim of highlighting their methodological strengths and weaknesses, as well as their clinical usefulness.
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How to Cite
Faita, F. (2020). Deep learning in Emergency Medicine: Recent contributions and methodological challenges. Emergency Care Journal, 16(1). https://doi.org/10.4081/ecj.2020.8573
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