Cited by 678Digital Radiography and Breast ImagingAI in cancer detectionGlobal Cancer Incidence and Screening

The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.

Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection

Open access

Lehman, Constance D., Wellman, Robert D., Buist, Diana S. M., Kerlikowske, Karla, Tosteson, Anna N. A., Miglioretti, Diana L.·2015·JAMA Internal Medicine

DOI 10.1001/jamainternmed.2015.5231PMC PMC4836172

Open this paper in CoChat — free account Open PDF

Abstract

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

Crossref · 10.1001/jamainternmed.2015.5231

Cites

44 of 44 references verified0 citation errors · 0 not checkable

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The Statistical Evaluation of Medical Tests for Classification and Prediction
PEPE, M. S. (2003) · doi:10.1093/oso/9780198509844.001.0001 · Cited by 2,206
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
The Statistical Evaluation of Medical Tests for Classification and Prediction
PEPE, M. S. (2005) · Technometrics · doi:10.1198/tech.2005.s278 · Cited by 1,907
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
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
Screening Mammography with Computer-aided Detection: Prospective Study of 12,860 Patients in a Community Breast Center
Freer, T. W., & Ulissey, M. J. (2001) · Radiology · doi:10.1148/radiol.2203001282 · Cited by 748
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
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
Changes in Breast Cancer Detection and Mammography Recall Rates After the Introduction of a Computer-Aided Detection System
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
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

Show all 44 references

Sorted by citations

Items 101–200 of 676

System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning–Directed Clinical Evaluations During Radiation and Chemoradiation
Hong, J. C., Eclov, N. C. W., Dalal, N., Thomas, S. M., Stephens, S. J., Malicki, M., Shields, S., Cobb, A., Mowery, Y. M., Niedzwiecki, D., et al. (2020) · Journal of Clinical Oncology · doi:10.1200/jco.20.01688 · Cited by 93
Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: a diagnostic accuracy study
Oakden‐Rayner, L., Gale, W., Bonham, T. A., Lungren, M. P., Carneiro, G., Bradley, A. P., & Palmer, L. J. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00004-8 · Cited by 101
Will Machine Learning Tip the Balance in Breast Cancer Screening?
Trister, A. D., Buist, D. S. M., & Lee, C. I. (2017) · JAMA Oncology · doi:10.1001/jamaoncol.2017.0473 · Cited by 92
Self-citation
Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions
Taylor, C. R., Monga, N., Johnson, C., Hawley, J. R., & Patel, M. J. (2023) · Diagnostics · doi:10.3390/diagnostics13122041 · Cited by 103
Deep Learning-Based Artificial Intelligence for Mammography
Yoon, J. H., & Kim, E. (2021) · Korean Journal of Radiology · doi:10.3348/kjr.2020.1210 · Cited by 95
Computer-aided diagnosis: A survey with bibliometric analysis
Takahashi, R., & Kajikawa, Y. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.02.004 · Cited by 89
Automated Breast Cancer Diagnosis Using Deep Learning and Region of Interest Detection (BC-DROID)
Platania, R., Shams, S., Yang, S., Zhang, J., Lee, K., & Park, S. (2017) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3107411.3107484 · Cited by 87
Reduction of False-Positive Markings on Mammograms: a Retrospective Comparison Study Using an Artificial Intelligence-Based CAD
Mayo, R. C., Kent, D., Sen, L. Q. C., Kapoor, M. M., Leung, J. W. T., & Watanabe, A. T. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-018-0168-6 · Cited by 87
Advances of AI in image-based computer-aided diagnosis: A review
Yeasmin, M. N., Amin, M. A., Joti, T. J., Aung, Z., & Azim, M. A. (2024) · Array · doi:10.1016/j.array.2024.100357 · Cited by 105
A review of computer aided detection in mammography
Katzen, J. T., & Dodelzon, K. (2018) · Clinical Imaging · doi:10.1016/j.clinimag.2018.08.014 · Cited by 83
Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network
Petrini, D. G. P., Shimizu, C., Roela, R. A., Valente, G. V., Folgueira, M. A. A. K., & Kim, H. Y. (2022) · IEEE Access · doi:10.1109/access.2022.3193250 · Cited by 88
Artificial Intelligence Algorithm Improves Radiologist Performance in Skeletal Age Assessment: A Prospective Multicenter Randomized Controlled Trial
Eng, D. K.-M., Khandwala, N., Long, J., Fefferman, N. R., Lala, S. V., Strubel, N. A., Milla, S. S., Filice, R. W., Sharp, S. E., Towbin, A. J., et al. (2021) · Radiology · doi:10.1148/radiol.2021204021 · Cited by 88
Artificial Intelligence in Breast Cancer Screening
Potnis, K. C., Ross, J. S., Aneja, S., Gross, C. P., & Richman, I. B. (2022) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2022.4969 · Cited by 94
Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence
Tran, W. T., Naini, A. S., Lu, F., Gandhi, S., Meti, N., Brackstone, M., Rakovitch, E., & Curpen, B. N. (2020) · Canadian Association of Radiologists Journal · doi:10.1177/0846537120949974 · Cited by 83
[Retracted] Enhanced Watershed Segmentation Algorithm‐Based Modified ResNet50 Model for Brain Tumor Detection
Sharma, A. K., Nandal, A., Dhaka, A., Koundal, D., Bogatinoska, D. C., & Alyami, H. (2022) · BioMed Research International · doi:10.1155/2022/7348344 · Cited by 80
Retracted
Artificial Intelligence (AI) for Screening Mammography, From the AJR Special Series on AI Applications
Lamb, L. R., Lehman, C. D., Gastounioti, A., Conant, E. F., & Bahl, M. (2022) · American Journal of Roentgenology · doi:10.2214/ajr.21.27071 · Cited by 86
Self-citation
Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy
Choi, J., Shin, K., Jung, J., Bae, H., Kim, D. H., Byeon, J.-S., & Kim, N. (2020) · Clinical Endoscopy · doi:10.5946/ce.2020.054 · Cited by 78
Texture analysis imaging “what a clinical radiologist needs to know”
Corrias, G., Micheletti, G., Barberini, L., Suri, J. S., & Saba, L. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.110055 · Cited by 76
Hallmarks of artificial intelligence contributions to precision oncology
Chang, T., Park, S., Schäffer, A. A., Jiang, P., & Ruppin, E. (2025) · Nature Cancer · doi:10.1038/s43018-025-00917-2 · Cited by 83
Double reading in breast cancer screening: considerations for policy-making
Taylor‐Phillips, S., & Stinton, C. (2019) · British Journal of Radiology · doi:10.1259/bjr.20190610 · Cited by 74
Prevalence in Visual Search: From the Clinic to the Lab and Back Again
Horowitz, T. S. (2017) · Japanese Psychological Research · doi:10.1111/jpr.12153 · Cited by 71
The proof of the pudding: in praise of a culture of real-world validation for medical artificial intelligence
Cabitza, F., & Zeitoun, J.-D. (2019) · Annals of Translational Medicine · doi:10.21037/atm.2019.04.07 · Cited by 72
Stand-alone artificial intelligence - The future of breast cancer screening?
Sechopoulos, I., & Mann, R. M. (2020) · The Breast · doi:10.1016/j.breast.2019.12.014 · Cited by 71
Concurrent Computer-Aided Detection Improves Reading Time of Digital Breast Tomosynthesis and Maintains Interpretation Performance in a Multireader Multicase Study
Benedikt, R. A., Boatsman, J. E., Swann, C. A., Kirkpatrick, A. D., & Toledano, A. Y. (2017) · American Journal of Roentgenology · doi:10.2214/ajr.17.18185 · Cited by 70
The Doctor-Patient Relationship With Artificial Intelligence
Aminololama‐Shakeri, S., & López, J. E. (2018) · American Journal of Roentgenology · doi:10.2214/ajr.18.20509 · Cited by 74
Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women
Sasaki, M., Tozaki, M., Rodríguez‐Ruiz, A., Yotsumoto, D., Ichiki, Y., Terawaki, A., Oosako, S., Sagara, Y., & Sagara, Y. (2020) · Breast Cancer · doi:10.1007/s12282-020-01061-8 · Cited by 67
The seven key challenges for the future of computer-aided diagnosis in medicine
Yanase, J., & Triantaphyllou, E. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.06.017 · Cited by 72
Machine Learning in Medicine
Deo, R. C. (2020) · Circulation · doi:10.1161/circulationaha.120.050583 · Cited by 78
Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms
Sharma, N., Ng, A. Y., James, J. J., Khara, G. S., Ambrózay, É., Austin, C. C., Forrai, G., Fox, G., Glocker, B., Heindl, A., et al. (2023) · BMC Cancer · doi:10.1186/s12885-023-10890-7 · Cited by 67
Diagnostic accuracy of deep learning in orthopaedic fractures: a systematic review and meta-analysis
Yang, S., Yin, B., Cao, W., Feng, C., Fan, G., & He, S. (2020) · Clinical Radiology · doi:10.1016/j.crad.2020.05.021 · Cited by 67
Utilization of Computer-Aided Detection for Digital Screening Mammography in the United States, 2008 to 2016
Keen, J. D., Keen, J. M., & Keen, J. E. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.08.033 · Cited by 62
A Comparison of Techniques for Class Imbalance in Deep Learning Classification of Breast Cancer
Walsh, R., & Tardy, M. (2022) · Diagnostics · doi:10.3390/diagnostics13010067 · Cited by 69
Regulatory Issues and Challenges to Artificial Intelligence Adoption
Harvey, H. B., & Gowda, V. (2021) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2021.07.007 · Cited by 59
Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement
Mori, Y., East, J. E., Hassan, C., Halvorsen, N., Berzin, T. M., Byrne, M. F., Renteln, D. A. V., Hewett, D. G., Repici, A., Ramchandani, M. J., et al. (2023) · Digestive Endoscopy · doi:10.1111/den.14531 · Cited by 62
Artificial Intelligence: A Primer for Breast Imaging Radiologists
Bahl, M. (2020) · Journal of Breast Imaging · doi:10.1093/jbi/wbaa033 · Cited by 67
Two-Stage Deep Learning Method for Breast Cancer Detection Using High-Resolution Mammogram Images
Ibrokhimov, B., & Kang, J.-Y. (2022) · Applied Sciences · doi:10.3390/app12094616 · Cited by 57
Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks
Kooi, T., & Karssemeijer, N. (2017) · Journal of medical imaging · doi:10.1117/1.jmi.4.4.044501 · Cited by 61
Deep learning in breast radiology: current progress and future directions
Ou, W. C., Polat, D. S., & Doğan, B. E. (2021) · European Radiology · doi:10.1007/s00330-020-07640-9 · Cited by 57
AI shows promise for breast cancer screening
Pisano, E. D. (2020) · Nature · doi:10.1038/d41586-019-03822-8 · Cited by 58
Improving the Performance of Radiologists Using Artificial Intelligence-Based Detection Support Software for Mammography: A Multi-Reader Study
Lee, J. H., Kim, K. H., Lee, E. H., Ahn, J. S., Ryu, J. K., Park, Y. M., Shin, G. W., Kim, Y. J., & Choi, H. Y. (2022) · Korean Journal of Radiology · doi:10.3348/kjr.2021.0476 · Cited by 61
Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis—a narrative review
Cè, M., Caloro, E., Pellegrino, M. E., Basile, M., Sorce, A., Fazzini, D., Oliva, G., & Cellina, M. I. (2022) · Exploration of Targeted Anti-tumor Therapy · doi:10.37349/etat.2022.00113 · Cited by 59
Artificial intelligence in breast cancer screening: primary care provider preferences
Hendrix, N., Hauber, B., Lee, C. I., Bansal, A., & Veenstra, D. L. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa292 · Cited by 52
Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review
Lokaj, B., Pugliese, M., Kinkel, K., Lovis, C., & Schmid, J. (2023) · European Radiology · doi:10.1007/s00330-023-10181-6 · Cited by 57
Towards improved breast mass detection using dual-view mammogram matching
Yan, Y., Conze, P.-H., Lamard, M., Quellec, G., Cochener, B., & Coatrieux, G. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102083 · Cited by 53
Integration of Artificial Intelligence Decision Aids to Reduce Workload and Enhance Efficiency in Thyroid Nodule Management
Tong, W., Wu, S.-H., Cheng, M., Huang, H., Liang, J., Li, C., Guo, H., He, D., Liu, Y., Xiao, H., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.13674 · Cited by 52
Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting
Leming, M., Bron, E. E., Bruffaerts, R., Ou, Y., Iglesias, J. E., Gollub, R. L., & Im, H. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00868-x · Cited by 63
Quantitative evaluation of Saliency-Based Explainable artificial intelligence (XAI) methods in Deep Learning-Based mammogram analysis
Cerekci, E. A., Alis, D. C., Denizoğlu, N., Camurdan, O., Şeker, M. E., Özer, C., Hansu, M. Y., Tanyel, T., Öksüz, İ., & Karaarslan, E. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111356 · Cited by 62
The Rebirth of CAD: How Is Modern AI Different from the CAD We Know?
Oakden‐Rayner, L. (2019) · Radiology Artificial Intelligence · doi:10.1148/ryai.2019180089 · Cited by 51
Diagnostic Performance of AI for Cancers Registered in A Mammography Screening Program: A Retrospective Analysis
Yirgin, İ. K., Köylüoğlu, Y. O., Şeker, M. E., Gürdal, S. Ö., Özaydın, A., Özçınar, B., Cabıoğlu, N., Özmen, V., & Arıbal, E. (2022) · Technology in Cancer Research & Treatment · doi:10.1177/15330338221075172 · Cited by 58
Evaluation of machine learning solutions in medicine
Antoniou, T., & Mamdani, M. (2021) · Canadian Medical Association Journal · doi:10.1503/cmaj.210036 · Cited by 58
Artificial intelligence in multiparametric magnetic resonance imaging: A review
Li, C., Li, W., Liu, C., Zheng, H., Cai, J., & Wang, S. (2022) · Medical Physics · doi:10.1002/mp.15936 · Cited by 52
Applications of Artificial Intelligence in Breast Imaging
Morgan, M. B., & Mates, J. (2020) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2020.08.007 · Cited by 53
Accurate Screening for Early-Stage Breast Cancer by Detection and Profiling of Circulating Tumor Cells
Crook, T., Leonard, R. C., Mokbel, K., Thompson, A. M., Michell, M. J., Page, R. L., Vaid, A. K., Mehrotra, R., Ranade, A. A., Limaye, S. A., et al. (2022) · Cancers · doi:10.3390/cancers14143341 · Cited by 49
Mucins as Potential Biomarkers for Early Detection of Cancer
Gautam, S. K., Κhan, P., Natarajan, G., Atri, P., Aithal, A., Ganti, A. K., Batra, S. K., Nasser, M. W., & Jain, M. (2023) · Cancers · doi:10.3390/cancers15061640 · Cited by 51
Should artificial intelligence be used in conjunction with neuroimaging in the diagnosis of Alzheimer’s disease?
Mirkin, S., & Albensi, B. C. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1094233 · Cited by 46
Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review
Maile, H. P., Li, J.-P. O., Gore, D. M., Leucci, M. T., Mulholland, P. J., Hau, S., Szabó, A., Moghul, I., Balaskas, K., Fujinami, K., et al. (2021) · JMIR Medical Informatics · doi:10.2196/27363 · Cited by 46
To pay or not to pay for artificial intelligence applications in radiology
Lobig, F., Subramanian, D., Blankenburg, M., Sharma, A., Variyar, A., & Butler, O. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00861-4 · Cited by 50
Expression of Concern: A gingivitis identification method based on contrast‐limited adaptive histogram equalization, gray‐level co‐occurrence matrix, and extreme learning machine
Li, W., Chen, Y., Sun, W., Brown, M., Zhang, X., Wang‎, S., & Miao, L. (2018) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22298 · Cited by 46
Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review
Bagchi, S., Tay, K. G., Huong, A. K. C., & Debnath, S. K. (2020) · International Journal of Electrical and Computer Engineering (IJECE) · doi:10.11591/ijece.v10i3.pp2336-2348 · Cited by 46
Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging
Park, J. E., Kickingereder, P., & Kim, H. S. (2020) · Korean Journal of Radiology · doi:10.3348/kjr.2019.0847 · Cited by 50
Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis
Liu, J., Lei, J., Ou, Y., Zhao, Y., Tuo, X., Zhang, B., & Shen, M. (2022) · Clinical and Experimental Medicine · doi:10.1007/s10238-022-00895-0 · Cited by 50
Impact of Early Breast Cancer Screening on Mortality Among Young Survivors of Childhood Hodgkin’s Lymphoma
Hodgson, D. C., Cotton, C. A., Crystal, P., & Nathan, P. C. (2016) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djw010 · Cited by 45
Pathway to Demonstrating Clinical Efficacy of Microwave Breast Imaging: Qualitative and Quantitative Performance Assessment
Porter, E., & O’Loughlin, D. (2022) · IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology · doi:10.1109/jerm.2022.3218756 · Cited by 44
Artificial intelligence for breast cancer: Implications for diagnosis and management
AlSamhori, J. F., AlSamhori, A. R. F., Duncan, L. A., Qalajo, A., Alshahwan, H. F., Al-abbadi, M., Soudi, M. A., Zakraoui, R., AlSamhori, A. F., AlRyalat, S. A., et al. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100120 · Cited by 50
Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review
Masud, R., Al-Rei, M., & Lokker, C. (2019) · JMIR Medical Informatics · doi:10.2196/12660 · Cited by 45
Importance of Better Human-Computer Interaction in the Era of Deep Learning: Mammography Computer-Aided Diagnosis as a Use Case
Nishikawa, R. M., & Bae, K. T. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.08.027 · Cited by 44
Systematic Review of Artificial Intelligence for Abnormality Detection in High-volume Neuroimaging and Subgroup Meta-analysis for Intracranial Hemorrhage Detection
Agarwal, S., Wood, D. A., Grzeda, M. T., Suresh, C., Din, M., Cole, J. H., Marc Modat, A., & Booth, T. C. (2023) · Clinical Neuroradiology · doi:10.1007/s00062-023-01291-1 · Cited by 52
256 Shades of gray: uncertainty and diagnostic error in radiology
Bruno, M. A. (2017) · Diagnosis · doi:10.1515/dx-2017-0006 · Cited by 43
Demystifying machine learning: a primer for physicians
Scott, I. A. (2021) · Internal Medicine Journal · doi:10.1111/imj.15200 · Cited by 44
A deep learning classifier for digital breast tomosynthesis
Ricciardi, R., Mettivier, G., Staffa, M., Sarno, A., Acampora, G., Minelli, S., Santoro, A., Antignani, E., Orientale, A., Pilotti, I. A. M., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.021 · Cited by 43
Impact of artificial intelligence in breast cancer screening with mammography
Dang, L.-A., Chazard, E., Poncelet, É., Serb, T., Rusu, A., Pauwels, X., Parsy, C., Poclet, T., Cauliez, H., Engelaere, C., et al. (2022) · Breast Cancer · doi:10.1007/s12282-022-01375-9 · Cited by 53
Impact of AI for Digital Breast Tomosynthesis on Breast Cancer Detection and Interpretation Time
Park, E. K., Kwak, S., Lee, W., Choi, J. S., Kooi, T., & Kim, E. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.230318 · Cited by 42
Double Reading in Breast Cancer Screening: Cohort Evaluation in the CO-OPS Trial
Taylor‐Phillips, S., Jenkinson, D., Stinton, C., Wallis, M., Dunn, J., & Clarke, A. (2018) · Radiology · doi:10.1148/radiol.2018171010 · Cited by 42
Range of Radiologist Performance in a Population-based Screening Cohort of 1 Million Digital Mammography Examinations
Salim, M., Dembrower, K., Eklund, M., Lindholm, P., & Strand, F. (2020) · Radiology · doi:10.1148/radiol.2020192212 · Cited by 42
Studying human-AI collaboration protocols: the case of the Kasparov’s law in radiological double reading
Cabitza, F., Campagner, A., & Sconfienza, L. M. (2021) · Health Information Science and Systems · doi:10.1007/s13755-021-00138-8 · Cited by 47
Deep Learning-Based Approach for the Diagnosis of Moyamoya Disease
Akiyama, Y., Mikami, T., & Mikuni, N. (2020) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2020.105322 · Cited by 40
Impact of explainable artificial intelligence assistance on clinical decision-making of novice dental clinicians
Glick, A., Clayton, M., Angelov, N., & Chang, J. T. (2022) · JAMIA Open · doi:10.1093/jamiaopen/ooac031 · Cited by 39
Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review
Li, L. T., Haley, L. C., Boyd, A. K., & Bernstam, E. V. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104531 · Cited by 49
Will AI Replace Ophthalmologists?
Korot, E., Wagner, S. K., Faes, L., Liu, X., Huemer, J. C., Ferraz, D., Keane, P. A., & Balaskas, K. (2020) · Translational Vision Science & Technology · doi:10.1167/tvst.9.2.2 · Cited by 39
Leveraging physiology and artificial intelligence to deliver advancements in health care
Zhang, A., Wu, Z., Wu, E., Wu, M. A., Snyder, M. P., Zou, J., & Wu, J. C. (2023) · Physiological Reviews · doi:10.1152/physrev.00033.2022 · Cited by 44
Implementation of a national AI technology program on cardiovascular outcomes and the health system
Fairbairn, T. A., Mullen, L., Nicol, E., Lip, G. Y. H., Schmitt, M., Shaw, M., Tidbury, L. P., Kemp, I., Crooks, J., Burnside, G., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03620-y · Cited by 40
Association of Microcalcification Clusters with Short-term Invasive Breast Cancer Risk and Breast Cancer Risk Factors
Ali, M. A., Czene, K., Hall, P. N., & Humphreys, K. (2019) · Scientific Reports · doi:10.1038/s41598-019-51186-w · Cited by 38
Predictive Performance of Cell-Free Nucleic Acid-Based Multi-Cancer Early Detection Tests: A Systematic Review
LeeVan, E., & Pinsky, P. F. (2023) · Clinical Chemistry · doi:10.1093/clinchem/hvad134 · Cited by 39
Measurements, Algorithms, and Presentations of Reality: Framing Interactions with AI-Enabled Decision Support
van Berkel, N., Bellio, M., Skov, M. B., & BLANDFORD, A. E. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3571815 · Cited by 42
Artificial intelligence and breast screening: French Radiology Community position paper
Thomassin‐Naggara, I., Balleyguier, C., Ceugnart, L., Heid, P., Lenczner, G., Maire, A., Séradour, B., Verzaux, L., & Taourel, P. (2019) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2019.08.005 · Cited by 38
Impact of artificial intelligence on pathologists’ decisions: an experiment
Meyer, J. M., Khademi, A., Têtu, B., Han, W., Nippak, P. M. D., & Remisch, D. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac103 · Cited by 39
Mammogram classification based on a novel convolutional neural network with efficient channel attention
Lou, Q., Li, Y., Qian, Y., Lu, F., & Ma, J. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106082 · Cited by 39
Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype
Furtney, I., Bradley, R. M., & Kabuka, M. R. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3290394 · Cited by 36
Shotgun proteomics coupled to nanoparticle-based biomarker enrichment reveals a novel panel of extracellular matrix proteins as candidate serum protein biomarkers for early-stage breast cancer detection
Fredolini, C., Pathak, K. V., Paris, L., Chapple, K. M., Tsantilas, K. A., Rosenow, M. A., Tegeler, T. J., Garcia‐Mansfield, K., Tamburro, D., Zhou, W., et al. (2020) · Breast Cancer Research · doi:10.1186/s13058-020-01373-9 · Cited by 36
Machine learning: principles and applications for thoracic surgery
Ostberg, N. P., Zafar, M. A., & Elefteriades, J. A. (2021) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezab095 · Cited by 38
Artificial intelligence in radiology: the ecosystem essential to improving patient care
Sogani, J., Allen, B., Dreyer, K., & McGinty, G. (2020) · Clinical Imaging · doi:10.1016/j.clinimag.2019.08.001 · Cited by 36
Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Diagnosis for Breast Microcalcification on Mammography
Do, Y. A., Jang, M., Yun, B. L., Shin, S. U., Kim, B., & Kim, S. M. (2021) · Diagnostics · doi:10.3390/diagnostics11081409 · Cited by 35
Deep learning in breast imaging
Bhowmik, A., & Eskreis‐Winkler, S. (2022) · BJR|Open · doi:10.1259/bjro.20210060 · Cited by 37
Convolutional-neural-network-based radiographs evaluation assisting in early diagnosis of the periodontal bone loss via periapical radiograph
Chen, I.-H., Lin, C.-H., Lee, M.-K., Chen, T.-E., Lan, T., Chang, C., Tseng, T.-Y., Wang, T., & Du, J. (2023) · Journal of Dental Sciences · doi:10.1016/j.jds.2023.09.032 · Cited by 34
CoroNet: Deep Neural Network-Based End-to-End Training for Breast Cancer Diagnosis
Mobark, N., Hamad, S. H., & Rida, S. Z. (2022) · Applied Sciences · doi:10.3390/app12147080 · Cited by 38
A cost-effective handheld breast scanner for use in low-resource environments: a validation study
Broach, R. B., Geha, R. C., Englander, B. S., DeLaCruz, L., Thrash, H., & Brooks, A. D. (2016) · World Journal of Surgical Oncology · doi:10.1186/s12957-016-1022-2 · Cited by 33
Impact of real-life use of artificial intelligence as support for human reading in a population-based breast cancer screening program with mammography and tomosynthesis
Elías-Cabot, E., Romero-Martín, S., Raya-Povedano, J. L., Brehl, A.-K., & Benito, M. Á. (2023) · European Radiology · doi:10.1007/s00330-023-10426-4 · Cited by 39
Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer
Benning, L., Peintner, A., & Peintner, L. (2022) · Cancers · doi:10.3390/cancers14030623 · Cited by 34
Beyond fine-tuning: Classifying high resolution mammograms using function-preserving transformations
Wei, T., Avilés-Rivero, A. I., Wang, S., Huang, Y., Gilbert, F. J., Schönlieb, C., & Chen, C. W. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102618 · Cited by 32
Deep learning for the radiographic diagnosis of proximal femur fractures: Limitations and programming issues
Guy, S., Jacquet, C., Tsenkoff, D., Argenson, J., & Ollivier, M. P. (2021) · Orthopaedics & Traumatology Surgery & Research · doi:10.1016/j.otsr.2021.102837 · Cited by 30

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: Lehman, Constance D.
Wellman, Robert D.
Buist, Diana S. M.
Kerlikowske, Karla
Tosteson, Anna N. A.
Miglioretti, Diana L.
Venue: JAMA Internal Medicine
Publisher: American Medical Association (AMA)
Published: 2015-11-01
Volume: 175
Issue: 11
Pages: 1828
ISSN: 2168-6106 (ISSN-L)
DOI: 10.1001/jamainternmed.2015.5231
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 › Radiology, Nuclear Medicine and Imaging › 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

Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks