دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: Radiologist Preferences for Artificial Intelligence-Based Decision Support During Screening Mammography Interpretation.
المؤلفون: Hendrix N; Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts., Lowry KP; Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, Washington. Electronic address: kplowry@uw.edu., Elmore JG; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California., Lotter W; Chief Technology Officer, DeepHealth Inc, RadNet AI Solutions, Cambridge, Massachusetts., Sorensen G; Chief Technology Officer, DeepHealth Inc, RadNet AI Solutions, Cambridge, Massachusetts., Hsu W; Department of Radiological Sciences, Data Integration, Architecture, and Analytics Group, University of California, Los Angeles, California; American Medical Informatics Association: Member, Governance Committee; RSNA: Deputy Editor, Radiology: Artificial Intelligence., Liao GJ; Department of Radiology, Virginia Mason Medical Center, Seattle, Washington., Parsian S; Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, Washington; Department of Radiology, Kaiser Permanente Washington, Seattle, Washington., Kolb S; Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, Washington., Naeim A; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California; Chief Medical Officer for Clinical Research, UCLA Health; Codirector: Clinical and Translational Science Institute and Center for SMART Health; Associate Director: Institute for Precision Health, Jonsson Comprehensive Cancer Center, Garrick Institute for Risk Sciences., Lee CI; Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, Washington; Department of Health Services, School of Public Health, University of Washington, Seattle, Washington; and Deputy Editor, JACR.
المصدر: Journal of the American College of Radiology : JACR [J Am Coll Radiol] 2022 Oct; Vol. 19 (10), pp. 1098-1110. Date of Electronic Publication: 2022 Aug 13.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Elsevier Country of Publication: United States NLM ID: 101190326 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1558-349X (Electronic) Linking ISSN: 15461440 NLM ISO Abbreviation: J Am Coll Radiol Subsets: MEDLINE
أسماء مطبوعة: Original Publication: New York, NY : Elsevier, c2004-
مواضيع طبية MeSH: Breast Neoplasms*/diagnostic imaging , Mammography*/methods, Artificial Intelligence ; Early Detection of Cancer/methods ; Female ; Humans ; Mass Screening ; Radiologists
مستخلص: 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 <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.
Conclusion: 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.
(Copyright © 2022. Published by Elsevier Inc.)
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معلومات مُعتمدة: P01 CA154292 United States CA NCI NIH HHS; R37 CA240403 United States CA NCI NIH HHS; TL1 TR002318 United States TR NCATS NIH HHS; UL1 TR002319 United States TR NCATS NIH HHS; KL2 TR002317 United States TR NCATS NIH HHS
فهرسة مساهمة: Keywords: Artificial intelligence; breast cancer; cancer screening; discrete choice experiment; preferences
تواريخ الأحداث: Date Created: 20220815 Date Completed: 20221013 Latest Revision: 20231002
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC9840464
DOI: 10.1016/j.jacr.2022.06.019
PMID: 35970474
قاعدة البيانات: MEDLINE
الوصف
تدمد:1558-349X
DOI:10.1016/j.jacr.2022.06.019