Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection
Open accessAbstract
IMPORTANCE: After the US Food and Drug Administration (FDA) approved computer-aided detection (CAD) for mammography in 1998, and the Centers for Medicare and Medicaid Services (CMS) provided increased payment in 2002, CAD technology disseminated rapidly. Despite sparse evidence that CAD improves accuracy of mammographic interpretations and costs over $400 million a year, CAD is currently used for most screening mammograms in the United States. OBJECTIVE: To measure performance of digital screening mammography with and without CAD in US community practice. DESIGN, SETTING, AND PARTICIPANTS: We compared the accuracy of digital screening mammography interpreted with (n = 495 818) vs without (n = 129 807) CAD from 2003 through 2009 in 323 973 women. Mammograms were interpreted by 271 radiologists from 66 facilities in the Breast Cancer Surveillance Consortium. Linkage with tumor registries identified 3159 breast cancers in 323 973 women within 1 year of the screening. MAIN OUTCOMES AND MEASURES: Mammography performance (sensitivity, specificity, and screen-detected and interval cancers per 1000 women) was modeled using logistic regression with radiologist-specific random effects to account for correlation among examinations interpreted by the same radiologist, adjusting for patient age, race/ethnicity, time since prior mammogram, examination year, and registry. Conditional logistic regression was used to compare performance among 107 radiologists who interpreted mammograms both with and without CAD. RESULTS: Screening performance was not improved with CAD on any metric assessed. Mammography sensitivity was 85.3% (95% CI, 83.6%-86.9%) with and 87.3% (95% CI, 84.5%-89.7%) without CAD. Specificity was 91.6% (95% CI, 91.0%-92.2%) with and 91.4% (95% CI, 90.6%-92.0%) without CAD. There was no difference in cancer detection rate (4.1 in 1000 women screened with and without CAD). Computer-aided detection did not improve intraradiologist performance. Sensitivity was significantly decreased for mammograms interpreted with vs without CAD in the subset of radiologists who interpreted both with and without CAD (odds ratio, 0.53; 95% CI, 0.29-0.97). CONCLUSIONS AND RELEVANCE: Computer-aided detection does not improve diagnostic accuracy of mammography. These results suggest that insurers pay more for CAD with no established benefit to women.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Rejection bias refers to conscious or unconscious desensitization to excessive alerts, and alert fatigue is a manifestation of this bias, as clinicians may ignore important alerts owing to an overwhelming number of false alarms.
“Rejection bias refers to the conscious or unconscious desensitization to excessive alerts. Alert fatigue is a manifestation of this bias, as clinicians may ignore important alerts owing to an overwhelming number of false alarms [81, 82].”
From Fairness of artificial intelligence in healthcare: review and recommendations (2024) · cites this paper as [81]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The Statistical Evaluation of Medical Tests for Classification and Prediction
PEPE, M. S. (2003) · doi:10.1093/oso/9780198509844.001.0001 · Cited by 2,221 · see citing works
Breast imaging reporting and data system (BI-RADS)
Liberman, L., & Menell, J. H. (2002) · Radiologic Clinics of North America · doi:10.1016/s0033-8389(01)00017-3 · Cited by 2,159 · see citing works
The Statistical Evaluation of Medical Tests for Classification and Prediction
PEPE, M. S. (2005) · Technometrics · doi:10.1198/tech.2005.s278 · Cited by 1,917 · see citing works
A hierarchical regression approach to meta‐analysis of diagnostic test accuracy evaluations
Rutter, C. M., & Gatsonis, C. (2001) · Statistics in Medicine · doi:10.1002/sim.942 · Cited by 1,510 · see citing works
Breast Imaging Reporting and Data System
Berg, W. A., Campassi, C., LANGENBERG, P. W., & Sexton, M. J. (2000) · American Journal of Roentgenology · doi:10.2214/ajr.174.6.1741769 · Cited by 1,268 · see citing works
Freer, T. W., & Ulissey, M. J. (2001) · Radiology · doi:10.1148/radiol.2203001282 · Cited by 753 · see citing works
Influence of Computer-Aided Detection on Performance of Screening Mammography
Fenton, J. J., Taplin, S. H., Carney, P. A., Abraham, L., Sickles, E. A., D’Orsi, C. J., Berns, E. A., Cutter, G., Hendrick, R. E., Barlow, W. E., et al. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa066099 · Cited by 619 · see citing works
Improving breast cancer diagnosis with computer-aided diagnosis
Jiang, Y., Nishikawa, R. M., Schmidt, R. A., Metz, C. E., Giger, M. L., & Doi, K. (1999) · Academic Radiology · doi:10.1016/s1076-6332(99)80058-0 · Cited by 310 · see citing works
Gur, D., Sumkin, J. H., Rockette, H. E., Ganott, M. A., Hakim, C. M., Hardesty, L. A., Poller, W. R., Shah, R., & Wallace, L. P. (2004) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djh067 · Cited by 306 · see citing works
Single Reading with Computer-Aided Detection for Screening Mammography
Gilbert, F. J., Astley, S., Gillan, M. G., Agbaje, O. F., Wallis, M., James, J. J., Boggis, C. R. M., & Duffy, S. W. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0803545 · Cited by 296 · see citing works
Screening Mammograms: Interpretation with Computer-aided Detection—Prospective Evaluation
Morton, M. J., Whaley, D. H., Brandt, K. R., & Amrami, K. K. (2006) · Radiology · doi:10.1148/radiol.2392042121 · Cited by 218 · see citing works
Computer-aided Detection with Screening Mammography in a University Hospital Setting
Birdwell, R. L., Bandodkar, P., & Ikeda, D. M. (2005) · Radiology · doi:10.1148/radiol.2362040864 · Cited by 187 · see citing works
Effectiveness of Computer-Aided Detection in Community Mammography Practice
Fenton, J. J., Abraham, L., Taplin, S. H., Geller, B. M., Carney, P. A., D’Orsi, C. J., Elmore, J. G., & Barlow, W. E. (2011) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djr206 · Cited by 168 · see citing works
Taylor, P., & POTTS, H. W. W. (2008) · European Journal of Cancer · doi:10.1016/j.ejca.2008.02.016 · Cited by 160 · see citing works
Impact of Computer-Aided Detection in a Regional Screening Mammography Program
Cupples, T. E., Cunningham, J. E., & Reynolds, J. C. (2005) · American Journal of Roentgenology · doi:10.2214/ajr.04.1300 · Cited by 149 · see citing works
Gromet, M. (2008) · American Journal of Roentgenology · doi:10.2214/ajr.07.2812 · Cited by 144 · see citing works
Prospective Assessment of Computer-Aided Detection in Interpretation of Screening Mammography
Ko, J., Nicholas, M. J., Mendel, J. B., & Slanetz, P. J. (2006) · American Journal of Roentgenology · doi:10.2214/ajr.05.1582 · Cited by 121 · see citing works
How Widely Is Computer-Aided Detection Used in Screening and Diagnostic Mammography?
Rao, V. M., Levin, D. C., Parker, L., Cavanaugh, B. C., Frangos, A. J., & Sunshine, J. H. (2010) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2010.05.019 · Cited by 103 · see citing works
Gilbert, F. J., Astley, S., McGee, M. A., Gillan, M. G., Boggis, C. R. M., Griffiths, P. M., & Duffy, S. W. (2006) · Radiology · doi:10.1148/radiol.2411051092 · Cited by 97 · see citing works
Association between Mammography Timing and Measures of Screening Performance in the United States
Yankaskas, B. C., Taplin, S. H., Ichikawa, L., Geller, B. M., Rosenberg, R. D., Carney, P. A., Kerlikowske, K. M., Ballard-Barbash, R. R., Cutter, G., & Barlow, W. E. (2005) · Radiology · doi:10.1148/radiol.2342040048 · Cited by 95 · see citing works
Self-citationCan Computer-aided Detection Be Detrimental to Mammographic Interpretation?
Philpotts, L. E. (2009) · Radiology · doi:10.1148/radiol.2531090689 · Cited by 87 · see citing works
Ciatto, S., Turco, M. R. D., Risso, G. G., Catarzi, S., Bonardi, R., Viterbo, V., Gnutti, P., Guglielmoni, B., Pinelli, L., Pandiscia, A., et al. (2003) · European Journal of Radiology · doi:10.1016/s0720-048x(02)00011-6 · Cited by 85 · see citing works
Improving Breast Imaging Quality Standards
National Cancer Policy Board (2005) · National Academies Press eBooks · doi:10.17226/11308 · Cited by 71 · see citing works
Impact of Computer-Aided Detection Systems on Radiologist Accuracy With Digital Mammography
Cole, E. B., Zhang, Z., Marques, H. S., Hendrick, R. E., Yaffe, M. J., & Pisano, E. D. (2014) · American Journal of Roentgenology · doi:10.2214/ajr.12.10187 · Cited by 69 · see citing works
Nishikawa, R. M., Giger, M. L., Doi, K., Vyborny, C. J., & Schmidt, R. A. (1993) · Medical Physics · doi:10.1118/1.596952 · Cited by 68 · see citing works
The Cost of Breast Cancer Screening in the Medicare Population
Gross, C. P., Long, J. B., Ross, J. S., Abu-Khalaf, M. M., Wang, R., Killelea, B. K., Gold, H. T., Chagpar, A. B., & Ma, X. (2013) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2013.1397 · Cited by 68 · see citing works
Short-Term Outcomes of Screening Mammography Using Computer-Aided Detection
Fenton, J. J., Xing, G., Elmore, J. G., Bang, H., Chen, S. L., Lindfors, K. K., & Baldwin, L. M. (2013) · Annals of Internal Medicine · doi:10.7326/0003-4819-158-8-201304160-00002 · Cited by 63 · see citing works
The Preponderance of Evidence Supports Computer-aided Detection for Screening Mammography
Birdwell, R. L. (2009) · Radiology · doi:10.1148/radiol.2531090611 · Cited by 63 · see citing works
Sa, F., Sickles, E. A., Evans, W. P., & Linver, M. N. (2004) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djh257 · Cited by 56 · see citing works
Taplin, S. H., Rutter, C. M., & Lehman, C. D. (2006) · American Journal of Roentgenology · doi:10.2214/ajr.05.0940 · Cited by 53 · see citing works
Self-citationBlinded Comparison of Computer-Aided Detection with Human Second Reading in Screening Mammography
Georgian-Smith, D., Moore, R. H., Halpern, E. F., Yeh, E. D., Rafferty, E. A., D’Alessandro, H. A., Staffa, M., Hall, D. A., McCarthy, K. A., & Kopans, D. B. (2007) · American Journal of Roentgenology · doi:10.2214/ajr.07.2393 · Cited by 53 · see citing works
Breast Imaging and Computer-Aided Detection
Hall, F. M. (2007) · New England Journal of Medicine · doi:10.1056/nejme078028 · Cited by 33 · see citing works
Computer-Aided Detection of Breast Cancer: Has Promise Outstripped Performance?
Elmore, J. G., & Carney, P. A. (2004) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djh049 · Cited by 22 · see citing works
Computer-Aided Screening Mammography
Ciatto, S., & Houssami, N. (2007) · New England Journal of Medicine · doi:10.1056/nejmc071248 · Cited by 16 · see citing works
Castellano, C. R., Almenar, A., Pinto, J. M., Varela, C., Muñoz, E., & López, M. C. B. (2011) · American Journal of Roentgenology · doi:10.2214/ajr.09.3408 · Cited by 15 · see citing works
Computer-aided screening mammography.
Ruiz, J. F. (2007) · PubMed · Cited by 15 · see citing works
Computer-aided screening mammography.
Feig, S. A., Birdwell, R. L., & Linver, M. N. (2007) · PubMed · Cited by 9 · see citing works
Computer-Assisted Detection and Screening Mammography: Where's the Beef?
Berry, D. A. (2011) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djr267 · Cited by 6 · see citing works
Blinded Comparison of Computer-Aided Detection with Human Second Reading in Screening Mammography
Birdwell, R. L. (2008) · Yearbook of Diagnostic Radiology · doi:10.1016/s0098-1672(08)79183-5 · Cited by 6 · see citing works
Re: Effectiveness of Computer-Aided Detection in Community Mammography Practice
Levman, J. E. D. (2011) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djr491 · Cited by 3 · see citing works
Re: Effectiveness of Computer-Aided Detection in Community Mammography Practice
Nishikawa, R. M., Giger, M. L., Jiang, Y., & Metz, C. E. (2011) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djr492 · Cited by 3 · see citing works
3–24 Prospective Assessment of Computer-Aided Detection in Interpretation of Screening Mammography
Ulissey, M. J. (2007) · Breast Diseases A Year Book Quarterly · doi:10.1016/s1043-321x(07)80318-3 · Cited by 2 · see citing works
Linver, M. N. (2006) · Breast Diseases A Year Book Quarterly · doi:10.1016/s1043-321x(06)80496-0 · Cited by 2 · see citing works
2–9 Computer-aided Detection With Screening Mammography in a University Hospital Setting
Burhenne, L. J. W. (2006) · Breast Diseases A Year Book Quarterly · doi:10.1016/s1043-321x(06)80418-2 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803 · see citing works
International evaluation of an AI system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287 · see citing works
Key challenges for delivering clinical impact with artificial intelligence
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019) · BMC Medicine · doi:10.1186/s12916-019-1426-2 · Cited by 3,136 · see citing works
Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817 · see citing works
Deep Learning in Medical Imaging: General Overview
Lee, J., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017) · Korean Journal of Radiology · doi:10.3348/kjr.2017.18.4.570 · Cited by 1,287 · see citing works
Deep Learning to Improve Breast Cancer Detection on Screening Mammography
Shen, L., Margolies, L. R., Rothstein, J. H., Fluder, E., McBride, R., & Sieh, W. (2019) · Scientific Reports · doi:10.1038/s41598-019-48995-4 · Cited by 1,110 · see citing works
Large scale deep learning for computer aided detection of mammographic lesions
Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., den Heeten, A., & Karssemeijer, N. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.07.007 · Cited by 965 · see citing works
Deep Learning in Medical Image Analysis
Chan, H., Samala, R. K., Hadjiiski, L. M., & Zhou, C. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-33128-3_1 · Cited by 783 · see citing works
Detecting and classifying lesions in mammograms with Deep Learning
Ribli, D., Horváth, A., Unger, Z., Pollner, P., & Csabai, I. (2018) · Scientific Reports · doi:10.1038/s41598-018-22437-z · Cited by 696 · see citing works
Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening
Wu, N., Phang, J., Park, J., Shen, Y., Huang, Z., Zorin, M., Jastrzȩbski, S., Févry, T., Katsnelson, J., Kim, E. S., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2945514 · Cited by 695 · see citing works
Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T. H., Chevalier, M., Tan, T., Mertelmeier, T., et al. (2019) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/djy222 · Cited by 692 · see citing works
Deep neural network improves fracture detection by clinicians
Lindsey, R., Daluiski, A., Chopra, S., Lachapelle, A. S., Mozer, M. C., Sicular, S., Hanel, D. P., Gardner, M. J., Gupta, A., Hotchkiss, R. J., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1806905115 · Cited by 654 · see citing works
Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System
Rodríguez-Ruiz, A., Krupinski, E., Mordang, J.-J., Schilling, K., Heywang-Köbrunner, S. H., Sechopoulos, I., & Mann, R. M. (2019) · Radiology · doi:10.1148/radiol.2018181371 · Cited by 602 · see citing works
Fairness of artificial intelligence in healthcare: review and recommendations
Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., Fujima, N., et al. (2024) · Japanese Journal of Radiology · doi:10.1007/s11604-023-01474-3 · Cited by 607 · see citing works
Lång, K., Josefsson, V., Larsson, A.-M., Larsson, S., Högberg, C., Sartor, H., Hofvind, S., Andersson, I., & Rosso, A. (2023) · The Lancet Oncology · doi:10.1016/s1470-2045(23)00298-x · Cited by 598 · see citing works
Swanson, K., Wu, E., Zhang, A., Alizadeh, A. A., & Zou, J. (2023) · Cell · doi:10.1016/j.cell.2023.01.035 · Cited by 597 · see citing works
Kim, H.-E., Kim, H. H., Han, B., Kim, K., Han, K., Nam, H., Lee, E. H., & Kim, E. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30003-0 · Cited by 538 · see citing works
Lee, J., Kim, D., Jeong, S., & Choi, S. (2018) · Journal of Periodontal & Implant Science · doi:10.5051/jpis.2018.48.2.114 · Cited by 505 · see citing works
Vollmer, S. J., Mateen, B. A., Bohner, G., Király, F. J., Ghani, R., Jonsson, P. F., Cumbers, S., Jonas, A., McAllister, K., Myles, P. R., et al. (2020) · BMJ · doi:10.1136/bmj.l6927 · Cited by 554 · see citing works
Automated deep-neural-network surveillance of cranial images for acute neurologic events
Titano, J. J., Badgeley, M., Schefflein, J., Pain, M., Su, A., Cai, M., Swinburne, N., Zech, J., Kim, J., Bederson, J., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0147-y · Cited by 458 · see citing works
Role of artificial intelligence in patient safety outcomes: systematic literature review
Choudhury, A., & Asan, O. (2020) · JMIR Medical Informatics · doi:10.2196/18599 · Cited by 440 · see citing works
Lotter, W., Diab, A. R., Haslam, B., Kim, J. G., Grisot, G., Wu, E., Wu, K., Onieva, J. O., Boyer, Y., Boxerman, J. L., et al. (2021) · Nature Medicine · doi:10.1038/s41591-020-01174-9 · Cited by 423 · see citing works
Schaffter, T., Buist, D. S. M., Lee, C. I., Nikulin, Y., Ribli, D., Guan, Y., Lotter, W., Jie, Z., Du, H., Wang, S., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.0265 · Cited by 409 · see citing works
Self-citationDratsch, T., Chen, X., Rezazade Mehrizi, M., Kloeckner, R., Mähringer-Kunz, A., Püsken, M., Baeßler, B., Sauer, S., Maintz, D., & Pinto dos Santos, D. (2023) · Radiology · doi:10.1148/radiol.222176 · Cited by 369 · see citing works
Deep Learning in Medical Imaging
Kim, M., Yun, J., Cho, Y., Shin, K., Jang, R., Bae, H., & Kim, N. (2019) · Neurospine · doi:10.14245/ns.1938396.198 · Cited by 371 · see citing works
Breast cancer detection using deep learning: Datasets, methods, and challenges ahead
Din, N. M. U., Dar, R. A., Rasool, M., & Assad, A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106073 · Cited by 375 · see citing works
Computer‐aided diagnosis in the era of deep learning
Chan, H., Hadjiiski, L. M., & Samala, R. K. (2020) · Medical Physics · doi:10.1002/mp.13764 · Cited by 361 · see citing works
Gerke, S., Babic, B., Evgeniou, T., & Cohen, I. G. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0262-2 · Cited by 359 · see citing works
Peng, Y., Dharssi, S., Chen, Q., Keenan, T. D. L., Agrón, E., Wong, W. T., Chew, E. Y., & Lu, Z. (2018) · Ophthalmology · doi:10.1016/j.ophtha.2018.11.015 · Cited by 358 · see citing works
Sechopoulos, I., Teuwen, J., & Mann, R. M. (2020) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2020.06.002 · Cited by 364 · see citing works
Artificial intelligence and machine learning in cancer imaging
Koh, D., Papanikolaou, N. K., Bick, U., Illing, R. O., Kahn, C. E., Kalpathi-Cramer, J., Matos, C., Martí‐Bonmatí, L., Miles, A., Mun, S. K., et al. (2022) · Communications Medicine · doi:10.1038/s43856-022-00199-0 · Cited by 325 · see citing works
With an eye to AI and autonomous diagnosis
Keane, P. A., & Topol, E. J. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0048-y · Cited by 304 · see citing works
A systematic survey of computer-aided diagnosis in medicine: Past and present developments
Yanase, J., & Triantaphyllou, E. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112821 · Cited by 303 · see citing works
AI-based computer-aided diagnosis (AI-CAD): the latest review to read first
Fujita, H. (2020) · Radiological Physics and Technology · doi:10.1007/s12194-019-00552-4 · Cited by 308 · see citing works
Geras, K. J., Mann, R. M., & Moy, L. P. (2019) · Radiology · doi:10.1148/radiol.2019182627 · Cited by 298 · see citing works
Salim, M., Wåhlin, E., Dembrower, K., Azavedo, E., Foukakis, T., Liu, Y., Smith, K., Eklund, M., & Strand, F. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2020.3321 · Cited by 288 · see citing works
Sendak, M. P., Ratliff, W., Sarro, D., Alderton, E., Futoma, J., Gao, M., Nichols, M., Revoir, M., Yashar, F., Miller, C., et al. (2020) · JMIR Medical Informatics · doi:10.2196/15182 · Cited by 287 · see citing works
Toward robust mammography-based models for breast cancer risk
Yala, A., Mikhael, P. G., Strand, F., Lin, G., Smith, K., Wan, Y.-L., Lamb, L., Hughes, K., Lehman, C., & Barzilay, R. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.aba4373 · Cited by 287 · see citing works
Self-citationMachine learning in clinical decision making
Adlung, L., Cohen, Y., Mor, U., & Elinav, E. (2021) · Med · doi:10.1016/j.medj.2021.04.006 · Cited by 304 · see citing works
Artificial intelligence in breast imaging
Le, E., Wang, Y., Huang, Y., Hickman, S. E., & Gilbert, F. J. (2019) · Clinical Radiology · doi:10.1016/j.crad.2019.02.006 · Cited by 277 · see citing works
Deep convolutional neural networks for mammography: advances, challenges and applications
Abdelhafiz, D., Yang, C., Ammar, R., & Nabavi, S. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2823-4 · Cited by 274 · see citing works
Interpretive Error in Radiology
Waite, S., Scott, J. A., Gale, B. D., Fuchs, T., Kolla, S. S., & Reede, D. L. (2016) · American Journal of Roentgenology · doi:10.2214/ajr.16.16963 · Cited by 274 · see citing works
Will Artificial Intelligence Replace Radiologists?
Langlotz, C. P. (2019) · Radiology Artificial Intelligence · doi:10.1148/ryai.2019190058 · Cited by 254 · see citing works
Conant, E. F., Toledano, A. Y., Periaswamy, S., Fotin, S. V., Go, J., Boatsman, J. E., & Hoffmeister, J. W. (2019) · Radiology Artificial Intelligence · doi:10.1148/ryai.2019180096 · Cited by 243 · see citing works
How does artificial intelligence in radiology improve efficiency and health outcomes?
van Leeuwen, K. G., de Rooij, M., Schalekamp, S., van Ginneken, B., & Rutten, M. J. C. M. (2022) · Pediatric Radiology · doi:10.1007/s00247-021-05114-8 · Cited by 236 · see citing works
Framing the challenges of artificial intelligence in medicine
Yu, K.-H., & Kohane, I. S. (2019) · BMJ Quality & Safety · doi:10.1136/bmjqs-2018-008551 · Cited by 233 · see citing works
Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine
Ahn, J. S., Shin, S., Yang, S., Park, E. K., Kim, K. H., Cho, S. I., Ock, C., & Kim, S. (2023) · Journal of Breast Cancer · doi:10.4048/jbc.2023.26.e45 · Cited by 263 · see citing works
The role of artificial intelligence in medical imaging research
Tang, X. (2019) · BJR|Open · doi:10.1259/bjro.20190031 · Cited by 219 · see citing works
Radiological images and machine learning: Trends, perspectives, and prospects
Zhang, Z., & Sejdić, E. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.02.017 · Cited by 236 · see citing works
A Deep Learning Model to Triage Screening Mammograms: A Simulation Study
Yala, A., Schuster, T., Miles, R., Barzilay, R., & Lehman, C. (2019) · Radiology · doi:10.1148/radiol.2019182908 · Cited by 210 · see citing works
Self-citationShen, Y., Wu, N., Phang, J., Park, J., Liu, K., Tyagi, S., Heacock, L. C., Kim, S., Moy, L. P., Cho, K., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101908 · Cited by 214 · see citing works
Rodríguez‐Ruiz, A., Lång, K., Gubern‐Mérida, A., Teuwen, J., Broeders, M. J. M., Gennaro, G., Clauser, P., Helbich, T. H., Chevalier, M. L., Mertelmeier, T., et al. (2019) · European Radiology · doi:10.1007/s00330-019-06186-9 · Cited by 209 · see citing works
Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs
Dunnmon, J. A., Yi, D., Langlotz, C. P., Ré, C., Rubin, D. L., & Lungren, M. P. (2019) · Radiology · doi:10.1148/radiol.2018181422 · Cited by 206 · see citing works
Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis
Leibig, C., Brehmer, M., Bunk, S., Byng, D., Pinker, K., & Umutlu, L. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00070-x · Cited by 213 · see citing works
Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis
Levine, A., Schlosser, C. L., Grewal, J., Coope, R., Jones, S. J. M., & Yip, S. S. F. (2019) · Trends in cancer · doi:10.1016/j.trecan.2019.02.002 · Cited by 202 · see citing works
YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms
Hamed, G., Marey, M. A. E.-R., Amin, S. E.-S., & Tolba, M. F. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105823 · Cited by 204 · see citing works
Yoon, J. H., Strand, F., Baltzer, P., Conant, E. F., Gilbert, F. J., Lehman, C. D., Morris, E. A., Mullen, L. A., Nishikawa, R. M., Sharma, N., et al. (2023) · Radiology · doi:10.1148/radiol.222639 · Cited by 207 · see citing works
Self-citationJaremko, J. L., Azar, M., Bromwich, R. J., Lum, A., Cheong, L. H. A., Gibert, M., Laviolette, F., Gray, B., Reinhold, C., Cicero, M. D., et al. (2019) · Canadian Association of Radiologists Journal · doi:10.1016/j.carj.2019.03.001 · Cited by 200 · see citing works
Convolutional neural networks for breast cancer detection in mammography: A survey
Abdelrahman, L., Alghamdi, M., Collado‐Mesa, F., & Abdel-Mottaleb, M. S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104248 · Cited by 194 · see citing works
Raya-Povedano, J. L., Romero-Martín, S., Elías-Cabot, E., Gubern‐Mérida, A., Rodríguez‐Ruiz, A., & Benito, M. Á. (2021) · Radiology · doi:10.1148/radiol.2021203555 · Cited by 179 · see citing works
Skaane, P., Bandos, A. I., Niklason, L. T., Sebuødegård, S., Østerås, B. H., Gullien, R., Gur, D., & Hofvind, S. S. H. (2019) · Radiology · doi:10.1148/radiol.2019182394 · Cited by 173 · see citing works
CAD and AI for breast cancer—recent development and challenges
Chan, H., Samala, R. K., & Hadjiiski, L. M. (2019) · British Journal of Radiology · doi:10.1259/bjr.20190580 · Cited by 174 · see citing works
Deep Neural Networks With Region-Based Pooling Structures for Mammographic Image Classification
Shu, X., Zhang, L., Wang, Z., Lv, Q., & Zhang, Y. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2968397 · Cited by 170 · see citing works
Bai, J., Posner, R., Wang, T., Yang, C., & Nabavi, S. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102049 · Cited by 175 · see citing works
Application of Deep Learning in Breast Cancer Imaging
Balkenende, L., Teuwen, J., & Mann, R. M. (2022) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2022.02.003 · Cited by 174 · see citing works
Pacilè, S., Lopez, J., Chone, P., Bertinotti, T., Grouin, J. M., & Fillard, P. (2020) · Radiology Artificial Intelligence · doi:10.1148/ryai.2020190208 · Cited by 164 · see citing works
Radiomics Improves Cancer Screening and Early Detection
Gillies, R. J., & Schabath, M. B. (2020) · Cancer Epidemiology, Biomarkers & Prevention · doi:10.1158/1055-9965.epi-20-0075 · Cited by 159 · see citing works
AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms
Sun, H., Li, C., Liu, B., Liu, Z., Wang, M., Zheng, H., Feng, D. D., & Wang, S. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab5745 · Cited by 160 · see citing works
Barnett, A. J., Schwartz, F. R., Tao, C., Chen, C., Ren, Y., Lo, J. Y.-C., & Rudin, C. D. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00423-x · Cited by 162 · see citing works
Vasey, B., Ursprung, S., Beddoe, B., Taylor, E. H., Marlow, N., Bilbro, N., Watkinson, P., & McCulloch, P. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.1276 · Cited by 154 · see citing works
Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer
Ng, A. Y., Oberije, C., Ambrózay, É., Szabó, E., Serfőző, O., Karpati, E., Fox, G., Glocker, B., Morris, E. A., Forrai, G., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02625-9 · Cited by 154 · see citing works
Computer-aided breast cancer detection and classification in mammography: A comprehensive review
Loizidou, K., Elia, R., & Pitris, C. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106554 · Cited by 156 · see citing works
Photoacoustic imaging of breast cancer: a mini review of system design and image features
Nyayapathi, N., & Xia, J. (2019) · Journal of Biomedical Optics · doi:10.1117/1.jbo.24.12.121911 · Cited by 143 · see citing works
Zhang, J., Wu, J., Zhou, X. S., Shi, F., & Shen, D. G. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.09.001 · Cited by 145 · see citing works
Khan, M. H.-M., Boodoo-Jahangeer, N. B., Dullull, W., Nathire, S., Gao, X. W., Sinha, G. R., & Nagwanshi, K. K. (2021) · PLoS ONE · doi:10.1371/journal.pone.0256500 · Cited by 139 · see citing works
Inkpen, K., Chappidi, S., Mallari, K., Nushi, B., Ramesh, D., Michelucci, P., Mandava, V., Vepřek, L. H., & Quinn, G. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3534561 · Cited by 132 · see citing works
Governance of automated image analysis and artificial intelligence analytics in healthcare
Ho, C. W.-L., Soon, D. T. L., Caals, K., & Kapur, J. (2019) · Clinical Radiology · doi:10.1016/j.crad.2019.02.005 · Cited by 132 · see citing works
Artificial intelligence and deep learning – Radiology's next frontier?
Mayo, R. C., & Leung, J. W. T. (2017) · Clinical Imaging · doi:10.1016/j.clinimag.2017.11.007 · Cited by 128 · see citing works
Hickman, S. E., Woitek, R., Le, E., Im, Y. R., Luxhøj, C. M., Avilés-Rivero, A. I., Baxter, G. C., Mackay, J., & Gilbert, F. J. (2021) · Radiology · doi:10.1148/radiol.2021210391 · Cited by 126 · see citing works
Henriksen, E., Carlsen, J. F., Vejborg, I., Nielsen, M. B., & Lauridsen, C. A. (2018) · Acta Radiologica · doi:10.1177/0284185118770917 · Cited by 126 · see citing works
Gao, Y., Geras, K. J., Lewin, A. A., & Moy, L. P. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20392 · Cited by 125 · see citing works
Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study
Kim, E., Kim, H.-E., Han, K., Kang, B. J., Sohn, Y., Woo, O. H., & Lee, C. W. (2018) · Scientific Reports · doi:10.1038/s41598-018-21215-1 · Cited by 123 · see citing works
Mahmood, T., Li, J., Pei, Y., Akhtar, F., Rehman, M. U., & Wasti, S. H. (2022) · PLoS ONE · doi:10.1371/journal.pone.0263126 · Cited by 118 · see citing works
Promises and Perils of Artificial Intelligence in Neurosurgery
Panesar, S. S., Kliot, M., Parrish, R. G., Fernandez-Miranda, J. C., Cagle, Y. D., & Britz, G. W. (2019) · Neurosurgery · doi:10.1093/neuros/nyz471 · Cited by 116 · see citing works
Kohli, A., & Jha, S. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.12.029 · Cited by 114 · see citing works
Overview of Artificial Intelligence in Breast Cancer Medical Imaging
Zheng, D., He, X., & Jing, J. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12020419 · Cited by 113 · see citing works
Artificial Intelligence in Imaging: The Radiologist’s Role
Rubin, D. L. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.05.036 · Cited by 110 · see citing works
Hickman, S. E., Baxter, G. C., & Gilbert, F. J. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01333-w · Cited by 110 · see citing works
Teare, P., Fishman, M. D. C., Benzaquen, O., Toledano, E., & Elnekave, E. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-9993-2 · Cited by 108 · see citing works
New machine-learning technologies for computer-aided diagnosis
Lynch, C. J., & Liston, C. M. (2018) · Nature Medicine · doi:10.1038/s41591-018-0178-4 · Cited by 109 · see citing works
Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging
Fogliato, R., Chappidi, S., Lungren, M. P., Fisher, P., Wilson, D., Fitzke, M., Parkinson, M., Horvitz, E., Inkpen, K. M., & Nushi, B. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533193 · Cited by 115 · see citing works
Diagnosis and prognosis of breast cancer by high-performance serum metabolic fingerprints
Huang, Y., Du, S., Liu, J., Huang, W., Liu, W., Zhang, M., Li, N., Wang, R., Wu, J., Chen, W., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2122245119 · Cited by 107 · see citing works
RETRACTED ARTICLE: Deep learning approaches to detect breast cancer: a comprehensive review
Sharafaddini, A. M., Esfahani, K. K., & Mansouri, N. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20011-6 · Cited by 104 · see citing works
RetractedCharacterizing the clinical adoption of medical AI devices through U.S. insurance claims
Wu, K., Wu, E., Theodorou, B., Liang, W., Mack, C., Glass, L., Sun, J., & Zou, J. (2024) · NEJM AI · doi:10.1056/aioa2300030 · Cited by 103 · see citing works
González-Gonzalo, C., Thee, E. F., Klaver, C. C. W., Lee, A. Y., Schlingemann, R. O., Tufail, A., Verbraak, F., & Sánchez, C. I. (2022) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2021.101034 · Cited by 103 · see citing works
Artificial Intelligence for the Future Radiology Diagnostic Service
Mun, S. K., Wong, K. H., Lo, S. B., Li, Y., & Bayarsaikhan, S. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.614258 · Cited by 106 · see citing works
Tired in the Reading Room: The Influence of Fatigue in Radiology
Waite, S., Kolla, S. S., Jeudy, J. J., Legasto, A. C., Macknik, S. L., Martínez‐Conde, S., Krupinski, E. A., & Reede, D. L. (2016) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2016.10.009 · Cited by 100 · see citing works
Kooi, T., van Ginneken, B., Karssemeijer, N., & den Heeten, A. (2017) · Medical Physics · doi:10.1002/mp.12110 · Cited by 99 · see citing works
Watanabe, A. T., Lim, V., Vu, H., Chim, C. Y., Weise, E., Liu, J., Bradley, W. G., & Comstock, C. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00192-5 · Cited by 96 · see citing works
Hadjiiski, L. M., Cha, K., Chan, H., Drukker, K., Morra, L., Näppi, J. J., Sahiner, B., Yoshida, H., Chen, Q., Deserno, T. M., et al. (2022) · Medical Physics · doi:10.1002/mp.16188 · Cited by 102 · see citing works
- Authors
- Lehman, C. D., Wellman, R. D., Buist, D. S. M., Kerlikowske, K., Tosteson, A. N. A., & Miglioretti, D. L.
- Affiliations
- Massachusetts General Hospital (US)
- Kaiser Permanente Washington Health Research Institute (US)
- University of California, San Francisco (US)
- Dartmouth College (US)
- Corresponding author
- Lehman, Constance D.
- ORCID
- Venue
- JAMA Internal Medicine
- Publisher
- American Medical Association (AMA)
- Published
- 2015-11-01
- Volume
- 175
- Issue
- 11
- Pages
- 1828
- ISSN
- 2168-6106 (ISSN-L)
- PMC
- PMC4836172
- OpenAlex
- W1917647701
- Cited by
- 678
- References
- 44
- Citations by year
- 2026: 38 · 2025: 76 · 2024: 81 · 2023: 92 · 2022: 95 · 2021: 86 · 2020: 72 · 2019: 59 · 2018: 35 · 2017: 27 · 2016: 14 · 2015: 1
- FWCI
- 20.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Cancer Institute (HHSN261201100031C, U54CA163303, P30CA023108, P01CA154292)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Digital Radiography and Breast Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
- Keywords
- computer-aided detection, digital mammography, breast cancer screening, diagnostic accuracy, mammography sensitivity, screen-detected breast cancer, interval breast cancer, Breast Cancer Surveillance Consortium, community practice, radiologist interpretation, breast cancer detection rate, mammography quality assurance
- MeSH terms
- Adult, Aged, Aged, 80 and over, Breast Neoplasms, Female, Humans, Mammography, Mass Screening, Middle Aged, Radiographic Image Interpretation, Computer-Assisted, Registries, Sensitivity and Specificity, United States, Reproducibility of Results, Logistic Models, Early Detection of Cancer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
