The impact of forms of AI feedback and image quality on reporting radiographers trust and decision switching when interpreting plain radiographic images of the appendicular skeleton: European Congress of Radiology (2024)

Purpose or Learning ObjectiveReported accuracies and workforce shortages have increased integration of AI into clinical environments. Furthermore, radiographer reporting helps ease the burden of image reporting. ‘System trust’ is identified as a challenge to clinical AI integration. To the authors’ knowledge, no research has been conducted on the factors impacting reporting radiographers’ trust and decision making when using different forms of AI feedback.Methods or BackgroundTwelve reporting radiographers, three from each region of the UK, participated in this study. The Qualtrics® platform was used to randomly allocate 18 radiographic examinations to each participant. Participants were asked to locate any pathology and indicate their agreement with the AI localisation, represented by GradCAM heatmaps and the AI binary diagnosis. Spearman’s rho and Kendall’s tau were used to investigate any correlation between trust and agreement with various forms of AI feedback and initial image quality. Results or FindingsParticipants disagreed with the AI heatmaps for the abnormal examinations 45.8% (n=66 of 144 individual images) of the time and agreed with binary feedback on 86.7% of examinations (26 of 30 cases). 0.7% (n=2) indicated that they would decision switch following AI feedback. 22.2% (n=32) agreed with the localisation of pathology from the heatmap. Agreement with AI feedback was correlated with trust (-.515; -.584, significant large negative correlation (p=