. 2025 Apr;32(2):155-172.

doi: 10.1007/s10140-024-02306-1. Epub 2024 Dec 23.

Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities

David Dreizin¹, Garvit Khatri², Pedro V Staziaki³, Karen Buch⁴, Mathias Unberath⁵, Mohammed Mohammed⁶, Aaron Sodickson⁷, Bharti Khurana⁸, Anjali Agrawal⁹, James Stephen Spann¹⁰, Nicholas Beckmann¹¹, Zachary DelProposto¹², Christina A LeBedis¹³, Melissa Davis¹⁴, Gabrielle Dickerson¹⁵, Michael Lev¹⁶

Affiliations

¹ Emergency and Trauma Imaging, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA. daviddreizin@gmail.com.
² Abdominal Imaging, Department of Radiology, University of Colorado, Denver, CO, USA.
³ Cardiothoracic imaging, Department of Radiology, University of Vermont, Larner College of Medicine, Burlington, USA.
⁴ Neuroradiology imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
⁵ Johns Hopkins University, Baltimore, MD, USA.
⁶ Abdominal imaging, Department of Radiology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.
⁷ Mass General Brigham Enterprise Emergency Radiology, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
⁸ Trauma Imaging Research and innovation Center, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
⁹ Department of Radiology, Teleradiology Solutions, Delhi, India.
¹⁰ Department of Radiology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA.
¹¹ McGovern Medical School at UTHealth, Houston, USA.
¹² Division of Emergency Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
¹³ Department of Radiology, Boston Medical Center, Boston, MA, USA.
¹⁴ Department of Radiology, Yale University, New Haven, CT, USA.
¹⁵ University of Maryland School of Medicine, Baltimore, MD, USA.
¹⁶ Emergency Radiology, Department of Radiology, Massachusetts General Hospial, Boston, USA.

PMID: 39714735
PMCID: PMC12270498
DOI: 10.1007/s10140-024-02306-1

Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities

David Dreizin et al. Emerg Radiol. 2025 Apr.

. 2025 Apr;32(2):155-172.

doi: 10.1007/s10140-024-02306-1. Epub 2024 Dec 23.

Authors

Affiliations

¹ Emergency and Trauma Imaging, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA. daviddreizin@gmail.com.
² Abdominal Imaging, Department of Radiology, University of Colorado, Denver, CO, USA.
³ Cardiothoracic imaging, Department of Radiology, University of Vermont, Larner College of Medicine, Burlington, USA.
⁴ Neuroradiology imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
⁵ Johns Hopkins University, Baltimore, MD, USA.
⁶ Abdominal imaging, Department of Radiology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.
⁷ Mass General Brigham Enterprise Emergency Radiology, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
⁸ Trauma Imaging Research and innovation Center, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
⁹ Department of Radiology, Teleradiology Solutions, Delhi, India.
¹⁰ Department of Radiology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA.
¹¹ McGovern Medical School at UTHealth, Houston, USA.
¹² Division of Emergency Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
¹³ Department of Radiology, Boston Medical Center, Boston, MA, USA.
¹⁴ Department of Radiology, Yale University, New Haven, CT, USA.
¹⁵ University of Maryland School of Medicine, Baltimore, MD, USA.
¹⁶ Emergency Radiology, Department of Radiology, Massachusetts General Hospial, Boston, USA.

PMID: 39714735
PMCID: PMC12270498
DOI: 10.1007/s10140-024-02306-1

Erratum in

Correction to: Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities.
Dreizin D, Khatri G, Staziaki PV, Buch K, Unberath M, Mohammed M, Sodickson A, Khurana B, Agrawal A, Spann JS, Beckmann N, DelProposto Z, LeBedis CA, Davis M, Dickerson G, Lev M. Dreizin D, et al. Emerg Radiol. 2025 Apr;32(2):305-306. doi: 10.1007/s10140-025-02312-x. Emerg Radiol. 2025. PMID: 39821589 No abstract available.

Abstract

Background: Emergency/trauma radiology artificial intelligence (AI) is maturing along all stages of technology readiness, with research and development (R&D) ranging from data curation and algorithm development to post-market monitoring and retraining.

Purpose: To develop an expert consensus document on best research practices and methodological priorities for emergency/trauma radiology AI.

Methods: A Delphi consensus exercise was conducted by the ASER AI/ML expert panel between 2022-2024. In phase 1, a steering committee (7 panelists) established key themes- curation; validity; human factors; workflow; barriers; future avenues; and ethics- and generated an edited, collated long-list of statements. In phase 2, two Delphi rounds using anonymous RAND/UCLA Likert grading were conducted with web-based data capture (round 1) and a bespoke excel document with literature hyperlinks (round 2). Between rounds, editing and knowledge synthesis helped maximize consensus. Statements reaching ≥80% agreement were included in the final document.

Results: Delphi rounds 1 and 2 consisted of 81 and 78 items, respectively.18/21 expert panelists (86%) responded to round 1, and 15 to round 2 (17% drop-out). Consensus was reached for 65 statements. Observations were summarized and contextualized. Statements with unanimous consensus centered around transparent methodologic reporting; testing for generalizability and robustness with external data; and benchmarking performance with appropriate metrics and baselines. A manuscript draft was circulated to panelists for editing and final approval.

Conclusions: The document is meant as a framework to foster best-practices and further discussion among researchers working on various aspects of emergency and trauma radiology AI.

Keywords: ASER; Artificial intelligence; Computer aided detection; Consensus statement; Delphi study; Emergency; Emergency Radiology; Imaging; Machine learning; Position paper; Radiology; Research priorities; Trauma; Trauma radiology.

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Conflict of interest statement

Declarations. The authors have no conflicts of interest to declare that are relevant to the content of this article. This original work has not been published or submitted elsewhere for review.

Figures

**Fig. 1**
Delphi study flow-chart. Flow-chart of ASER AI/ML expert panel Delphi consensus approach

See this image and copyright information in PMC

References

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1. Dreizin D (2023) The American Society of Emergency Radiology (ASER) AI/ML expert panel: inception, mandate, work products, and goals. Emerg Radiol 30(3):279–283 - PubMed
1. Dreizin D, Staziaki PV, Khatri GD, Beckmann NM, Feng Z, Liang Y, Delproposto ZS, Klug M, Spann JS, Sarkar N (2023) Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel. Emerg Radiol 30(3):251–265 - PMC - PubMed
1. Agrawal A, Khatri GD, Khurana B, Sodickson AD, Liang Y, Dreizin D (2023) A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations. Emerg Radiol 30(3):267–277 - PMC - PubMed

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Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities

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Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities

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