TY - JOUR
T1 - Clinicians’ perspectives on the use of artificial intelligence to triage MRI brain scans
AU - Din, Munaib
AU - Daga, Karan
AU - Saoud, Jihad
AU - Wood, David
AU - Kierkegaard, Patrick
AU - Brex, Peter
AU - Booth, Thomas C
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/2
Y1 - 2025/2
N2 - Artificial intelligence (AI) tools can triage radiology scans to streamline the patient pathway and also relieve clinician workload. Validated AI tools can mitigate the delays in reporting scans by flagging time-sensitive and actionable findings. In this study, we aim to investigate current stakeholder perspectives and identify obstacles to integrating AI in clinical pathways. We created a survey to ascertain the perspectives of 133 clinicians across the United Kingdom regarding the acceptability of an AI tool that triages MRI brain scans into ‘normal’ and ‘abnormal’. As part of this survey, we supplied clinicians with information on training and validation case numbers, model performance, validation using unseen data, and explainability saliency maps. With regards to the specific use case of AI in MRI brain scans, 71% of respondents preferred the use of an AI-assisted triage compared to the current system without triage, typically chronologically. Notably, information that explained and helped visualise the AI model's decision making was found to improve clinician confidence. When shown a heatmap, 60% of participants felt more confident in the AI's decision. The results of this short communication demonstrate a positive support for the implementation of AI-assistive tools in triage.
AB - Artificial intelligence (AI) tools can triage radiology scans to streamline the patient pathway and also relieve clinician workload. Validated AI tools can mitigate the delays in reporting scans by flagging time-sensitive and actionable findings. In this study, we aim to investigate current stakeholder perspectives and identify obstacles to integrating AI in clinical pathways. We created a survey to ascertain the perspectives of 133 clinicians across the United Kingdom regarding the acceptability of an AI tool that triages MRI brain scans into ‘normal’ and ‘abnormal’. As part of this survey, we supplied clinicians with information on training and validation case numbers, model performance, validation using unseen data, and explainability saliency maps. With regards to the specific use case of AI in MRI brain scans, 71% of respondents preferred the use of an AI-assisted triage compared to the current system without triage, typically chronologically. Notably, information that explained and helped visualise the AI model's decision making was found to improve clinician confidence. When shown a heatmap, 60% of participants felt more confident in the AI's decision. The results of this short communication demonstrate a positive support for the implementation of AI-assistive tools in triage.
UR - http://www.scopus.com/inward/record.url?scp=85214584451&partnerID=8YFLogxK
U2 - 10.1016/j.ejrad.2025.111921
DO - 10.1016/j.ejrad.2025.111921
M3 - Article
SN - 0720-048X
VL - 183
JO - European journal of radiology
JF - European journal of radiology
M1 - 111921
ER -