Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities.
IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
David Dreizin, Garvit Khatri, Pedro V Staziaki, Karen Buch, Mathias Unberath, Mohammed Mohammed, Aaron Sodickson, Bharti Khurana, Anjali Agrawal, James Stephen Spann, Nicholas Beckmann, Zachary DelProposto, Christina A LeBedis, Melissa Davis, Gabrielle Dickerson, Michael Lev
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引用次数: 0
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.
期刊介绍：
To advance and improve the radiologic aspects of emergency careTo establish Emergency Radiology as an area of special interest in the field of diagnostic imagingTo improve methods of education in Emergency RadiologyTo provide, through formal meetings, a mechanism for presentation of scientific papers on various aspects of Emergency Radiology and continuing educationTo promote research in Emergency Radiology by clinical and basic science investigators, including residents and other traineesTo act as the resource body on Emergency Radiology for those interested in emergency patient care Members of the American Society of Emergency Radiology (ASER) receive the Emergency Radiology journal as a benefit of membership!
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