biostudies-literatureHendrix NNational Center for Advancing Translational SciencesAmerican Cancer SocietyNCATS NIH HHSUniversity of WashingtonNational Cancer InstituteNCI NIH HHSNational Institutes of Health1098-1110https://www.ebi.ac.uk/biostudies/studies/S-EPMC9840464biostudies-literatureUnknown19(10)<h4>Background</h4>Artificial intelligence (AI) may improve cancer detection and risk prediction during mammography screening, but radiologists' preferences regarding its characteristics and implementation are unknown.<h4>Purpose</h4>To quantify how different attributes of AI-based cancer detection and risk prediction tools affect radiologists' intentions to use AI during screening mammography interpretation.<h4>Materials and methods</h4>Through qualitative interviews with radiologists, we identified five primary attributes for AI-based breast cancer detection and four for breast cancer risk prediction. We developed a discrete choice experiment based on these attributes and invited 150 US-based radiologists to participate. Each respondent made eight choices for each tool between three alternatives: two hypothetical AI-based tools versus screening without AI. We analyzed samplewide preferences using random parameters logit models and identified subgroups with latent class models.<h4>Results</h4>Respondents (n = 66; 44% response rate) were from six diverse practice settings across eight states. Radiologists were more interested in AI for cancer detection when sensitivity and specificity were balanced (94% sensitivity with <25% of examinations marked) and AI markup appeared at the end of the hanging protocol after radiologists complete their independent review. For AI-based risk prediction, radiologists preferred AI models using both mammography images and clinical data. Overall, 46% to 60% intended to adopt any of the AI tools presented in the study; 26% to 33% approached AI enthusiastically but were deterred if the features did not align with their preferences.<h4>Conclusion</h4>Although most radiologists want to use AI-based decision support, short-term uptake may be maximized by implementing tools that meet the preferences of dissuadable users.Journal of the American College of Radiology : JACRRadiologist Preferences for Artificial Intelligence-Based Decision Support During Screening Mammography Interpretation.PMC9840464R37 CA240403KL2 TR002317P01 CA154292UL1 TR002319CSDG-21-078-01-CPSHTL1 TR002318Lowry KPHendrix NSorensen GLotter WElmore JGKolb SLee CILiao GJParsian SNaeim AHsu WfalseRadiologist Preferences for Artificial Intelligence-Based Decision Support During Screening Mammography Interpretation.<h4>Background</h4>Artificial intelligence (AI) may improve cancer detection and risk prediction during mammography screening, but radiologists' preferences regarding its characteristics and implementation are unknown.<h4>Purpose</h4>To quantify how different attributes of AI-based cancer detection and risk prediction tools affect radiologists' intentions to use AI during screening mammography interpretation.<h4>Materials and methods</h4>Through qualitative interviews with radiologists, we identified five primary attributes for AI-based breast cancer detection and four for breast cancer risk prediction. We developed a discrete choice experiment based on these attributes and invited 150 US-based radiologists to participate. Each respondent made eight choices for each tool between three alternatives: two hypothetical AI-based tools versus screening without AI. We analyzed samplewide preferences using random parameters logit models and identified subgroups with latent class models.<h4>Results</h4>Respondents (n = 66; 44% response rate) were from six diverse practice settings across eight states. Radiologists were more interested in AI for cancer detection when sensitivity and specificity were balanced (94% sensitivity with <25% of examinations marked) and AI markup appeared at the end of the hanging protocol after radiologists complete their independent review. For AI-based risk prediction, radiologists preferred AI models using both mammography images and clinical data. Overall, 46% to 60% intended to adopt any of the AI tools presented in the study; 26% to 33% approached AI enthusiastically but were deterred if the features did not align with their preferences.<h4>Conclusion</h4>Although most radiologists want to use AI-based decision support, short-term uptake may be maximized by implementing tools that meet the preferences of dissuadable users.2022-01-01T00:00:00Z2022 Oct2025-04-04T03:00:31.795Z2025-04-04T03:00:31.795ZS-EPMC98404643597047410.1016/j.jacr.2022.06.019