Radiologist Preferences for Artificial Intelligence-Based Decision Support During Screening Mammography Interpretation

Autor: Nathaniel Hendrix, Kathryn P. Lowry, Joann G. Elmore, William Lotter, Gregory Sorensen, William Hsu, Geraldine J. Liao, Sana Parsian, Suzanne Kolb, Arash Naeim, Christoph I. Lee
Přispěvatelé: Dermatology
Rok vydání: 2022
Předmět:
Zdroj: Hendrix, N, Lowry, K P, Elmore, J G, Lotter, W, Sorensen, G, Hsu, W, Liao, G J, Parsian, S, Kolb, S, Naeim, A & Lee, C I 2022, ' Radiologist Preferences for Artificial Intelligence-Based Decision Support During Screening Mammography Interpretation ', Journal of the American College of Radiology : JACR, vol. 19, no. 10, pp. 1098-1110 . https://doi.org/10.1016/j.jacr.2022.06.019
Journal of the American College of Radiology : JACR, 19(10), 1098-1110. Elsevier BV
ISSN: 1546-1440
1558-349X
Popis: BACKGROUND: Artificial intelligence (AI) may improve cancer detection and risk prediction during mammography screening, but radiologists' preferences regarding its characteristics and implementation are unknown. PURPOSE: To quantify how different attributes of AI-based cancer detection and risk prediction tools affect radiologists' intentions to use AI during screening mammography interpretation. MATERIALS AND METHODS: 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. RESULTS: 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
Databáze: OpenAIRE
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