Thoracic Radiologists' Versus Computer Scientists' Perspectives on the Future of Artificial Intelligence in Radiology.
Background: There is intense interest and speculation in the application of artificial intelligence (AI) to radiology. The goals of this investigation were (1) to assess thoracic radiologists' perspectives on the role and expected impact of AI in radiology, and (2) to compare radiologists' perspecti...
| Publicado en: | Journal of Thoracic Imaging Vol. 35; no. 4; pp. 255 - 260 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Jul2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144344604&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144344604 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08835993 N1M jtl: Journal of Thoracic Imaging issn: 08835993 maglogo: N pubinfo: dt: Jul2020 vid: 35 iid: 4 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 144344604 144344604 NLM31609778 144344604 10.1097/RTI.0000000000000453 NLM31609778 144344604 ppf: 255 ppct: 5 formats: tig: atl: Thoracic Radiologists' Versus Computer Scientists' Perspectives on the Future of Artificial Intelligence in Radiology. aug: au: Eltorai, Adam E.M. Bratt, Alexander K. Guo, Haiwei H. affil: Brown University Alpert Medical School, Providence, RI sug: subj: Attitude of Health Personnel Medical Informatics Artificial Intelligence Specialties, Medical Methods Specialties, Medical Trends Female Human Male Middle Age Comparative Studies Multicenter Studies Evaluation Research Validation Studies Middle Aged: 45-64 years Female Male ab: Background: There is intense interest and speculation in the application of artificial intelligence (AI) to radiology. The goals of this investigation were (1) to assess thoracic radiologists' perspectives on the role and expected impact of AI in radiology, and (2) to compare radiologists' perspectives with those of computer science (CS) experts working in the AI development.Methods: An online survey was developed and distributed to chest radiologists and CS experts at leading academic centers and societies, comparing their expectations of AI's influence on radiologists' jobs, job satisfaction, salary, and role in society.Results: A total of 95 radiologists and 45 computer scientists responded. Computer scientists reported having read more scientific journal articles on AI/machine learning in the past year than radiologists (mean [95% confidence interval]=17.1 [9.01-25.2] vs. 7.3 [4.7-9.9], P=0.0047). The impact of AI in radiology is expected to be high, with 57.8% and 73.3% of computer scientists and 31.6% and 61.1% of chest radiologists predicting radiologists' job will be dramatically different in 5 to 10 years, and 10 to 20 years, respectively. Although very few practitioners in both fields expect radiologists to become obsolete, with 0% expecting radiologist obsolescence in 5 years, in the long run, significantly more computer scientists (15.6%) predict radiologist obsolescence in 10 to 20 years, as compared with 3.2% of radiologists reporting the same (P=0.0128). Overall, both chest radiologists and computer scientists are optimistic about the future of AI in radiology, with large majorities expecting radiologists' job satisfaction to increase or stay the same (89.5% of radiologists vs. 86.7% of CS experts, P=0.7767), radiologists' salaries to increase or stay the same (83.2% of radiologists vs. 73.4% of CS experts, P=0.1827), and the role of radiologists in society to improve or stay the same (88.4% vs. 86.7%, P=0.7857).Conclusions: Thoracic radiologists and CS experts are generally positive on the impact of AI in radiology. However, a larger percentage, but still small minority, of computer scientists predict radiologist obsolescence in 10 to 20 years. As the future of AI in radiology unfolds, this study presents a historical timestamp of which group of experts' perceptions were closer to eventual reality. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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