Work

Independent External Validation of Artificial Intelligence Algorithms for Automated Interpretation of Screening Mammography: A Systematic Review

Year: 2022

Type: review

Study design: Systematic Review

Source: Journal of the American College of Radiology

DOI: 10.1016/j.jacr.2021.11.008

Authors: Anna W. Anderson, M. Luke Marinovich, Nehmat Houssami, Kathryn P. Lowry, Joann G. Elmore, Diana S.M. Buist, Solveig Hofvind, Christoph I. Lee

Institutions: University of Washington, Curtin University, The University of Sydney, National Breast Cancer Foundation, Cancer Council NSW, University of California, Los Angeles, Kaiser Permanente Washington Health Research Institute, Cancer Registry of Norway

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: American Cancer Society, Kaiser Permanente, Kaiser Permanente Washington Health Research Institute, National Breast Cancer Foundation, University of Sydney, National Health and Medical Research Council, National Cancer Institute

Awards: G3506953032, G354197999, Comparative effectiveness of breast tomosynthesis and mammography in real-world population screening: Evidence to underpin and improve breast cancer screening, G5629183176, G5774533325, G68425887, Using Artificial Intelligence to Identify Women for High-Risk Screening MRI, Artificial Intelligence for Improved Breast Cancer Screening Accuracy: External Validation, Refinement, and Clinical Translation

OpenAlex ID: W4206777594

FWCI: 0.936

Cites: 28

Cited by: 61

Related to: 10