Julia E Goldberg1, Andrew B Rosenkrantz2. 1. Department of Radiology, NYU Langone Health, New York, NY. Electronic address: julia.goldberg@nyulangone.org. 2. Department of Radiology, NYU Langone Health, New York, NY.
Abstract
OBJECTIVE: To use Twitter to characterize public perspectives regarding artificial intelligence (AI) and radiology. METHODS AND MATERIALS: 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. RESULTS: 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 the issue of AI replacing radiologists, 77.5% leaned against AI replacing radiologists, 13.7% had a neutral view, and 8.8% leaned toward AI replacing radiologists. CONCLUSION: These observations provide an overview of the social media discussions regarding AI in radiology. While noting challenges, the discussions were overwhelmingly positive toward the transformative impact of AI on radiology and leaned against AI replacing radiologists. Greater radiologist engagement in this online social media dialog is encouraged.
OBJECTIVE: To use Twitter to characterize public perspectives regarding artificial intelligence (AI) and radiology. METHODS AND MATERIALS: 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. RESULTS: 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 the issue of AI replacing radiologists, 77.5% leaned against AI replacing radiologists, 13.7% had a neutral view, and 8.8% leaned toward AI replacing radiologists. CONCLUSION: These observations provide an overview of the social media discussions regarding AI in radiology. While noting challenges, the discussions were overwhelmingly positive toward the transformative impact of AI on radiology and leaned against AI replacing radiologists. Greater radiologist engagement in this online social media dialog is encouraged.
Authors: Philip Mulryan; Naomi Ni Chleirigh; Alexander T O'Mahony; Claire Crowley; David Ryan; Patrick McLaughlin; Mark McEntee; Michael Maher; Owen J O'Connor Journal: Insights Imaging Date: 2022-04-25