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Emergency Radiology

Erschienen in:

13.03.2023 | Original Article

A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations

verfasst von: Anjali Agrawal, Garvit D. Khatri, Bharti Khurana, Aaron D. Sodickson, Yuanyuan Liang, David Dreizin

Erschienen in: Emergency Radiology | Ausgabe 3/2023

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Abstract

Purpose

There is a growing body of diagnostic performance studies for emergency radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is known about user preferences, concerns, experiences, expectations, and the degree of penetration of AI tools in emergency radiology. Our aim is to conduct a survey of the current trends, perceptions, and expectations regarding AI among American Society of Emergency Radiology (ASER) members.

Methods

An anonymous and voluntary online survey questionnaire was e-mailed to all ASER members, followed by two reminder e-mails. A descriptive analysis of the data was conducted, and results summarized.

Results

A total of 113 members responded (response rate 12%). The majority were attending radiologists (90%) with greater than 10 years’ experience (80%) and from an academic practice (65%). Most (55%) reported use of commercial AI CAD tools in their practice. Workflow prioritization based on pathology detection, injury or disease severity grading and classification, quantitative visualization, and auto-population of structured reports were identified as high-value tasks. Respondents overwhelmingly indicated a need for explainable and verifiable tools (87%) and the need for transparency in the development process (80%). Most respondents did not feel that AI would reduce the need for emergency radiologists in the next two decades (72%) or diminish interest in fellowship programs (58%). Negative perceptions pertained to potential for automation bias (23%), over-diagnosis (16%), poor generalizability (15%), negative impact on training (11%), and impediments to workflow (10%).

Conclusion

ASER member respondents are in general optimistic about the impact of AI in the practice of emergency radiology and its impact on the popularity of emergency radiology as a subspecialty. The majority expect to see transparent and explainable AI models with the radiologist as the decision-maker.

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Titel: A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations
verfasst von: Anjali Agrawal
Garvit D. Khatri
Bharti Khurana
Aaron D. Sodickson
Yuanyuan Liang
David Dreizin
Publikationsdatum: 13.03.2023
Verlag: Springer International Publishing
Erschienen in: Emergency Radiology / Ausgabe 3/2023
Print ISSN: 1070-3004
Elektronische ISSN: 1438-1435
DOI: https://doi.org/10.1007/s10140-023-02121-0

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A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations