ARTIFICIAL INTELLIGENCE IN MAMMOGRAPHY FOR BREAST CANCER DETECTION: ETIOLOGY, DIAGNOSTICS, CLINICAL EFFICACY, AND CRITICAL APPRAISAL
Abstract
Background and Objective: Artificial intelligence (AI) technologies are increasingly reported as transformative tools in mammographic breast cancer screening; however, the existing literature is dominated by positive reporting, seldom critically interrogating methodological limitations, generalizability failures, or systemic barriers to deployment. This review aims to provide a balanced, critical appraisal of AI-assisted mammography by synthesizing evidence from large-scale clinical trials while explicitly evaluating the methodological weaknesses, dataset biases, workflow integration failures, and ethical gaps that constrain the translation of AI from controlled trial settings to routine clinical practice. A specific focus is placed on the implications for low-resource and non-Western healthcare settings, including Uzbekistan and Central Asia, where AI deployment faces unique structural and epidemiological challenges.
References
1. Lehman CD, Wellman RD, Buist DSM, et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Internal Medicine. 2015;175(11):1828–1837.
2. Sickles EA, D’Orsi CJ, Bassett LW, et al. ACR BI-RADS Mammography. 5th ed. American College of Radiology; 2013.
3. Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction. Radiology. 2019;292(1):60–66.
4. Rodríguez-Ruiz A, Krupinski E, Mordang JJ, et al. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology. 2019;290(2):305–314.
5. Dembrower K, Wåhlin E, Liu Y, et al. Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload. The Lancet Digital Health. 2020;2(9):e468–e474.
6. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89–94.
7. Lång K, Josefsson V, Larsson AM, et al. Artificial intelligence-supported screen reading versus standard double reading in the Transpara mammography screening trial. The Lancet Oncology. 2022;23(12):1471–1481.
8. Salim M, Wåhlin E, Dembrower K, et al. External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms. JAMA Oncology. 2020;6(10):1581–1588.
9. Leibig C, Brehmer M, Bunk S, et al. Combining the strengths of radiologists and AI for breast cancer screening. The Lancet Digital Health. 2022;4(7):e507–e519.
10. Kim HE, Kim HH, Han BK, et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence. The Lancet Digital Health. 2020;2(3):e138–e148.
11. Schaffter T, Buist DSM, Lee CI, et al. Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Network Open. 2020;3(3):e200265.
12. Ribli D, Horváth A, Unger Z, Pollner P, Csabai I. Detecting and classifying lesions in mammograms with Deep Learning. Scientific Reports. 2018;8(1):4165.
13. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine. 2019;25(1):44–56.
14. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates. CA: A Cancer Journal for Clinicians. 2021;71(3):209–249.
15. Bahl M. Artificial Intelligence: A Primer for Breast Imaging Radiologists. Journal of Breast Imaging. 2020;2(4):304–314.
16. Pacilè S, Lopez J, Chone P, et al. Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool. Radiology: Artificial Intelligence. 2020;2(6):e190208.
17. Sechopoulos I, Mann RM. Stand-alone artificial intelligence — The future of breast cancer screening? The Breast. 2020;49:254–260.
18. Gastounioti A, Desai S, Ahluwalia VS, Conant EF, Kontos D. Artificial intelligence in mammographic phenotyping of breast cancer risk. Breast Cancer Research. 2022;24(1):14.
19. Shen L, Margolies LR, Rothstein JH, Fluder E, McBride R, Sieh W. Deep Learning to Improve Breast Cancer Detection on Screening Mammography. Scientific Reports. 2019;9(1):12495.
20. World Health Organization. Breast cancer. https://www.who.int/news-room/fact-sheets/detail/breast-cancer. 2023.
21. Conant EF, Toledano AY, Station I, et al. Improving Accuracy in a Real-World Mammography Screening Program Using Artificial Intelligence. Radiology. 2019;291(3):602–609.
22. Kooi T, Litjens G, van Ginneken B, et al. Large scale deep learning for computer aided detection of mammographic lesions. Medical Image Analysis. 2017;35:303–312.
23. Wu N, Phang J, Park J, et al. Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening. IEEE Transactions on Medical Imaging. 2020;39(4):1184–1194.
24. Larson DB, Magnus DC, Lungren MP, Shah NH, Langlotz CP. Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence. Radiology. 2020;295(3):675–682.
25. Zahl PH, Mäkelä M, Welch HG. The natural history of invasive breast cancers detected by screening mammography. Archives of Internal Medicine. 2008;168(21):2311–2316.
26. Welch HG, Black WC. Overdiagnosis in cancer. Journal of the National Cancer Institute. 2010;102(9):605–613.
27. Raghu M, Zhang C, Kleinberg J, Bengio S. Transfusion: Understanding transfer learning for medical imaging. Advances in Neural Information Processing Systems. 2019;32.
28. Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ. The quantitative analysis of mammographic densities. Physics in Medicine & Biology. 1994;39(10):1629–1638.
