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

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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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Funding

David Dreizin funding source: NIH K08 EB027141-01A1 (PI: David Dreizin, MD)

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Authors and Affiliations

  1. New Delhi operations, Teleradiology Solutions, Delhi, India

    Anjali Agrawal

  2. Nuclear Medicine, Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA

    Garvit D. Khatri

  3. Emergency Radiology, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Bharti Khurana & Aaron D. Sodickson

  4. Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA

    Yuanyuan Liang

  5. Trauma and Emergency Radiology, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA

    David Dreizin

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  1. Anjali Agrawal
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Agrawal, A., Khatri, G.D., Khurana, B. et al. A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations. Emerg Radiol 30, 267–277 (2023). https://doi.org/10.1007/s10140-023-02121-0

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