Dembrower K, Wåhlin E, Liu Y, Salim M, Smith K, Lindholm P, Eklund M (2020) Fredrik Strand “Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study. Lancet Digital Health 2:e468–e474

Article  PubMed  Google Scholar 

Geras KJ, Mann RM, Moy L (2019) Artificial intelligence for mammography and digital breast tomosynthesis: current concepts and future perspectives. Radiology 293:246–259

Article  PubMed  Google Scholar 

Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

Article  PubMed  Google Scholar 

Le EPV, Wang Y, Huang Y, Hickman S, Gilbert FJ (2019) Artificial intelligence in breast imaging. Clin Radiol 74:357–366

Article  CAS  PubMed  Google Scholar 

Chan H-P, Samala RK, Hadjiiski LM (2020) CAD and AI for breast cancer-recent development and challenges. Br J Radiol 93:20190580

Article  PubMed  Google Scholar 

Bazzocchi M, Mazzarella F, Del Frate C, Girometti R, Zuiani C (2007) CAD Systems for mammography: a real opportunity? A review of the literature. Radiol med 112:329–353

Article  CAS  PubMed  Google Scholar 

Gur D, Sumkin JH (2006) CAD in screening mammography, AJR Women’s Imaging Commentary. AJR 187:1474

Azavedo E, Zackrisson S, Mejarè I, Arnlind MH (2012) Is single reading with computer-aided detection (CAD) as good as double reading in mammography screening? A systematic review. BMC Med Imag 12:22

Article  Google Scholar 

Dembrower K, Wåhlin E, Liu Y et al (2020) Effect of artificial intelligence based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study. Lancet Digit Health 2:e468–e474

Article  PubMed  Google Scholar 

Kyono T, Gilbert FJ, van der Schaar M (2020) Improving workflow efficiency for mammography using machine learning. J Am Coll Radiol 17:56–63

Article  PubMed  Google Scholar 

Raya-Povedano JL, Romero-Martín S, Elías-Cabot E, Gubern-Mérida A, Rodríguez-Ruiz A, Álvarez-Benito M (2021) AI-based strategies to reduce workload in breast cancer screening with mammography and tomosynthesis: a retrospective evaluation. Radiology 1:203555

Google Scholar 

Yala A, Schuster T, Miles R, Barzilay R, Lehman C (2019) A Deep learning model to triage screening mammograms: a simulation study. Radiology 293:38–46

Article  PubMed  Google Scholar 

Rodríguez-Ruiz Al et al (2019) Detection of breast cancer with mammography: effect of an artificial intelligence support system. Radiology 290(2):305–314

Article  PubMed  Google Scholar 

Rodriguez-Ruiz A, Lang K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, Helbich TH, Chevalier M, Tan T, Mertelmeier T, Wallis MG, Andersson I, Zackrisson S, Mann RM, Sechopoulos I (2019) Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. JNCI J Natl Cancer Inst 111(9):djy222

Article  Google Scholar 

Lång K, Dustler M, Dahlblom V, Åkesson A, Andersson I, Zackrisson S (2021) Identifying normal mammograms in a large screening population using artificial intelligence. Europ Radiol 31:1687–1692

Article  Google Scholar 

Leibig C, Brehmer M, Bunk S, Byng D, Pinker K, Umutlu L (2022) Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis. Lancet Digit Health 4:e507–e519

Article  CAS  PubMed  PubMed Central  Google Scholar 

Schaffter T, Buist DS, Lee CI et al (2020) Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw Open 3:e200265

Article  PubMed  PubMed Central  Google Scholar 

Wu N, Phang J, Park J et al (2019) Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Transact Med Imag 39:1184–1194

Article  Google Scholar 

Balta C, Rodriguez-Ruiz A, Mieskes C, Karssemeijer N, Heywang-Köbrunner S (2020) Going from double to single reading for screening exams labeled as likely normal by AI: What is the impact?: SPIE 11513 15th International Workshop on Breast Imaging (IWBI2020); May 22, (115130D)

McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, Chesus M, Corrado GS, Darzi A, Etemadi M, Garcia-Vicente F et al (2020) International evaluation of an AI system for breast cancer screening. Nature 577(2):89

Article  CAS  PubMed  Google Scholar 

Shoshan Y, Bakalo R, Gilboa-Solomon F, Ratner V, Barkan E, Ozery-Flato M, Amit M, Khapun D, Ambinder EB, Oluyemi ET, Panigrahi B, DiCarlo PA, Rosen-Zvi M, Mullen LA (2022) Artificial intelligence for reducing workload in breast cancer screening with digital breast tomosynthesis. Radiology 303:69–77

Article  PubMed  Google Scholar 

Taylor-Philips S, Freeman K (2022) Artificial intelligence to complement rather than replace radiologists in breast screening. The Lancet Digit Health 4(7):E478–E479

Article  Google Scholar 

Vicini S et al (2022) A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers. Radiol Med (Torino) 127(8):819–836

Article  PubMed  Google Scholar 

Houssami N, Kirkpatrick-Jones G, Noguchi N, Lee CI (2019) Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI’s potential in breast screening practice”e. Expert Rev Med Dev 16:351–362

Article  CAS  Google Scholar 

Lehman CD, Arao RF, Sprague BL et al (2017) National performance benchmarks for modern screening digital mammography: update from the Breast Cancer Surveillance Consortium. Radiology 283:49–58

Article  PubMed  Google Scholar 

American College of Radiology (2013) Breast imaging reporting and data system, 5th ed. Reston: American College of Radiology

Eng J. ROC analysis: web-based calculator for ROC curves. Baltimore: Johns Hopkins University [updated 2022 February 17]. Available from: http://www.jrocfit.org

McKinney SM, Sieniek M, Godbole V et al (2020) International evaluation of an AI system for breast cancer screening. Nature 577:89–94

Article  CAS  PubMed  Google Scholar 

Kim H-E, Kim HH, Han B-K, Kim KH, Han K, Nam H, Lee EH, Kim E-K (2020) Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective multi-reader study. Lancet Digit Health 2:138–148

Article  Google Scholar 

Guermazi A, Tannoury C, Kompel AJ, Murakami AM et al (2021) Improving radiographic fracture recognition performance and efficiency using artificial intelligence. Radiology 000:1–10

Google Scholar