Understanding and interpreting artificial intelligence, machine learning and deep learning in emergency medicine

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Ramlakhan, Shammi and Saatchi, Reza and Sabir, Lisa and Singh, Yardesh and Hughes, Ruby and Shobayo, Olamilekan and Ventour, Dale (2022) Understanding and interpreting artificial intelligence, machine learning and deep learning in emergency medicine. Emergency Medicine Journal, 39 (5). pp. 380-385. ISSN 1472-0213 (https://doi.org/10.1136/emermed-2021-212068)

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Abstract

The field of artificial intelligence (AI) has been developing more prominently for over half a century. Innovations in computer processing power and analytical capabilities coupled with the availability of huge amounts of routinely collected data has meant that AI research and technology development has grown exponentially in recent years. The results of this growth can be seen in emergency medicine (EM)—with the Food and Drug Administration approving the first AI software as a medical device for wrist fracture detection in 2018. As of 2021, several more have been approved—for triage, X-ray identification of pneumothorax and notification and triage software for CT images.

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