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United States newspaper and online media coverage of artificial intelligence and radiology from 1998 to 2023.
Zippi, Zachary D; Cortopassi, Isabel O; Grage, Rolf A; Johnson, Elizabeth M; McCann, Matthew R; Mergo, Patricia J; Sonavane, Sushilkumar K; Stowell, Justin T; White, Richard D; Little, Brent P.
Afiliación
  • Zippi ZD; Florida International University College of Medicine, United States of America.
  • Cortopassi IO; Mayo Clinic Florida and Mayo Clinic College of Medicine and Science, United States of America.
  • Grage RA; Mayo Clinic Florida and Mayo Clinic College of Medicine and Science, United States of America.
  • Johnson EM; Mayo Clinic Florida and Mayo Clinic College of Medicine and Science, United States of America.
  • McCann MR; Mayo Clinic Florida and Mayo Clinic College of Medicine and Science, United States of America.
  • Mergo PJ; Mayo Clinic Florida and Mayo Clinic College of Medicine and Science, United States of America.
  • Sonavane SK; Mayo Clinic Florida and Mayo Clinic College of Medicine and Science, United States of America.
  • Stowell JT; Mayo Clinic Florida and Mayo Clinic College of Medicine and Science, United States of America.
  • White RD; Mayo Clinic Florida and Mayo Clinic College of Medicine and Science, United States of America.
  • Little BP; Mayo Clinic Florida and Mayo Clinic College of Medicine and Science, United States of America. Electronic address: little.brent@mayo.edu.
Clin Imaging ; 113: 110238, 2024 Jul 20.
Article en En | MEDLINE | ID: mdl-39059086
ABSTRACT

OBJECTIVE:

To evaluate the frequency and content of media coverage pertaining to artificial intelligence (AI) and radiology in the United States from 1998 to 2023.

METHODS:

The ProQuest US Newsstream database was queried for print and online articles mentioning AI and radiology published between January 1, 1998, and March 30, 2023. A Boolean search using terms related to radiology and AI was used to retrieve full text and publication information. One of 9 readers with radiology expertise independently reviewed randomly assigned articles using a standardized scoring system.

RESULTS:

379 articles met inclusion criteria, of which 290 were unique and 89 were syndicated articles. Most had a positive sentiment (74 %) towards AI, while negative sentiment was far less common (9 %). Frequency of positive sentiment was highest in articles with a focus on AI and radiology (86 %) and lowest in articles focusing on AI and non-medical topics (55 %). The net impact of AI on radiology was most commonly presented as positive (60 %). Benefits of AI were more frequently mentioned (76 %) than potential harms (46 %). Radiologists were interviewed or quoted in less than one-third of all articles.

CONCLUSION:

Portrayal of the impact of AI on radiology in US media coverage was mostly positive, and advantages of AI were more frequently discussed than potential risks. However, articles with a general non-medical focus were more likely to have a negative sentiment regarding the impact of AI on radiology than articles with a more specific focus on medicine and radiology. Radiologists were infrequently interviewed or quoted in media coverage.
Palabras clave

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Clin Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Clin Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article