Evidence map›Paper›PMID 39714735›Full record

ArticleEmergency radiology2025

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

David Dreizin, Garvit Khatri, Pedro V Staziaki, Karen Buch, Mathias Unberath, Mohammed Mohammed, Aaron Sodickson, Bharti Khurana, Anjali Agrawal, James Stephen Spann and 6 more

Erratum issuedAbstract readConsensus Statement
PubMed Full text at PMC Publisher
In one paragraph

Article in Emergency radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

David DreizinEmergency 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.ORCID http://orcid.org/0000-0002-0176-0912
Garvit KhatriAbdominal Imaging, Department of Radiology, University of Colorado, Denver, CO, USA.
Pedro V StaziakiCardiothoracic imaging, Department of Radiology, University of Vermont, Larner College of Medicine, Burlington, USA.
Karen BuchNeuroradiology imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
Mathias UnberathJohns Hopkins University, Baltimore, MD, USA.
Mohammed MohammedAbdominal imaging, Department of Radiology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.
Aaron SodicksonMass General Brigham Enterprise Emergency Radiology, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
Bharti KhuranaTrauma Imaging Research and innovation Center, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
Anjali AgrawalDepartment of Radiology, Teleradiology Solutions, Delhi, India.
James Stephen SpannDepartment of Radiology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA.
Nicholas BeckmannMcGovern Medical School at UTHealth, Houston, USA.
Zachary DelPropostoDivision of Emergency Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
Christina A LeBedisDepartment of Radiology, Boston Medical Center, Boston, MA, USA.
Melissa DavisDepartment of Radiology, Yale University, New Haven, CT, USA.
Gabrielle DickersonUniversity of Maryland School of Medicine, Baltimore, MD, USA.
Michael LevEmergency Radiology, Department of Radiology, Massachusetts General Hospial, Boston, USA.

Funding

Human-centered CT-based CADx Tools for Traumatic Torso HemorrhageR01GM148987 · NIGMS · UNIVERSITY OF MARYLAND BALTIMORE · PI David Dreizin · 2023 to 2026
$1.5M
Foundation for the National Institutes of Health NIH R01 GM148987-01NIGMS NIH HHS R01 GM148987
6 · The paper itself

Abstract

backgroundEmergency/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.

methodsA 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.

resultsDelphi 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.

conclusionsThe 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.

Indexed as

Artificial IntelligenceRadiologyWounds and InjuriesDelphi TechniqueEmergency MedicineHumansArtificial intelligenceASERComputer aided detectionConsensus statementDelphi studyEmergencyEmergency RadiologyImagingMachine learningPosition paperRadiologyResearch prioritiesTraumaTrauma radiology

Identifiers

PMID39714735
PMCPMC12270498

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.