Artificial Intelligence and Radiology: A Social Media Perspective

Current Problems in Diagnostic Radiology
Julia E Goldberg, Andrew B Rosenkrantz

Abstract

To use Twitter to characterize public perspectives regarding artificial intelligence (AI) and radiology. Twitter was searched for all tweets containing the terms "artificial intelligence" and "radiology" from November 2016 to October 2017. Users posting the tweets, tweet content, and linked websites were categorized. Six hundred and five tweets were identified. These were from 407 unique users (most commonly industry-related individuals [22.6%]; radiologists only 9.3%) and linked to 216 unique websites. 42.5% of users were from the United States. The tweets mentioned machine/deep learning in 17.2%, industry in 14.0%, a medical society/conference in 13.4%, and a university in 9.8%. 6.3% mentioned a specific clinical application, most commonly oncology and lung/tuberculosis. 24.6% of tweets had a favorable stance regarding the impact of AI on radiology, 75.4% neutral, and none were unfavorable. 88.0% of linked websites leaned toward AI being positive for the field of radiology; none leaned toward AI being negative for the field. 51.9% of linked websites specifically mentioned improved efficiency for radiology with AI. 35.2% of websites described challenges for implementing AI in radiology. Of the 47.2% of websites that mentioned ...Continue Reading

Citations

Sep 18, 2020·The British Journal of Radiology·Natasha DavendralingamSusan C Shelmerdine
Dec 1, 2020·Current Problems in Diagnostic Radiology·Jessica T LovettVinay Prabhu
Apr 28, 2020·The Science of the Total Environment·Rajvikram Madurai Elavarasan, Rishi Pugazhendhi
May 1, 2021·Journal of Clinical Medicine·Anne MüllerFalk Schwendicke
Sep 22, 2021·European Radiology·Ling YangPasqualina Lina Santaguida

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