The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.
A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop
Open accessAbstract
Imaging research laboratories are rapidly creating machine learning systems that achieve expert human performance using open-source methods and tools. These artificial intelligence systems are being developed to improve medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection, computer-aided classification, and radiogenomics. In August 2018, a meeting was held in Bethesda, Maryland, at the National Institutes of Health to discuss the current state of the art and knowledge gaps and to develop a roadmap for future research initiatives. Key research priorities include: 1, new image reconstruction methods that efficiently produce images suitable for human interpretation from source data; 2, automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting; 3, new machine learning methods for clinical imaging data, such as tailored, pretrained model architectures, and federated machine learning methods; 4, machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence); and 5, validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets. This research roadmap is intended to identify and prioritize these needs for academic research laboratories, funding agencies, professional societies, and industry.
Overview
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
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The problem is exacerbated by issues related to interobserver and intraobserver variability of CT image interpretations rendered by radiologists, which can be as high as 37%.206”
From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [206]
Experimental — accuracy not yet measuredIn the citing paper
“This was largely due to advances in deep learning, the branch of AI that learns from large amounts of data.”
From Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology (2020) · cites this paper as [17]
In the citing paper
“In 2015, the ImageNet annual competition reached a milestone when the error rate of automatic classification of images dropped below 5%, which is the average human error rate (Figure S1) 17 .”
From Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology (2020) · cites this paper as [17]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Least Squares Support Vector Machine Classifiers
Suykens, J., & Vandewalle, J. (1999) · Neural Processing Letters · doi:10.1023/a:1018628609742 · Cited by 9,481
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
The Stanford CoreNLP Natural Language Processing Toolkit
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S. J., & McClosky, D. (2014) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.3115/v1/p14-5010 · Cited by 7,252
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
Self-citationThe WU-Minn Human Connectome Project: An overview
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651
A New Initiative on Precision Medicine
Collins, F. S., & Varmus, H. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500523 · Cited by 5,202
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107
In-Datacenter Performance Analysis of a Tensor Processing Unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al. (2017) · Proceedings of the 44th Annual International Symposium on Computer Architecture · doi:10.1145/3079856.3080246 · Cited by 4,537
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049
Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.78 · Cited by 2,780
Self-citationSavova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., & Chute, C. G. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.001560 · Cited by 1,967
2012 IEEE Conference on Computer Vision and Pattern Recognition
Loy, C., Hospedales, T. M., Xiang, T., & Gong, S. (2012) · Cited by 1,883
Implementing Machine Learning in Health Care — Addressing Ethical Challenges
Char, D. S., Shah, N. H., & Magnus, D. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1714229 · Cited by 1,857
Image reconstruction by domain-transform manifold learning
Zhu, B., Liu, J. Z., Cauley, S. F., Rosen, B. R., & Rosen, M. S. (2018) · Nature · doi:10.1038/nature25988 · Cited by 1,694
Proceedings of the 29th International Conference on Machine Learning (ICML-12)Preprint
Langford, J. C., & Pineau, J. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.4676 · Cited by 1,687
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Schlegl, T., Seeböck, P., Waldstein, S. M., Langs, G., & Schmidt-Erfurth, U. M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.010 · Cited by 1,645
Yang, Q., Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., Kalra, M. K., Zhang, Y., Sun, L., & Wang, G. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2827462 · Cited by 1,630
Self-citationRajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. P., et al. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002686 · Cited by 1,449
Self-citationA General Natural-language Text Processor for Clinical Radiology
Friedman, C., Alderson, P. O., Austin, J. H. M., Cimino, J. J., & Johnson, S. B. (1994) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.1994.95236146 · Cited by 775
the eMERGE Team, McCarty, C. A., Chisholm, R. L., Chute, C. G., Kullo, I. J., Jarvik, G. P., Larson, E. B., Li, R., Masys, D. R., Ritchie, M. D., et al. (2011) · BMC Medical Genomics · doi:10.1186/1755-8794-4-13 · Cited by 701
Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11723-8_9 · Cited by 609
Variability in Radiologists' Interpretations of Mammograms
Elmore, J. G., Wells, C. K., Lee, C., Howard, D. H., & Feinstein, A. R. (1994) · New England Journal of Medicine · doi:10.1056/nejm199412013312206 · Cited by 608
Yan, K., Wang, X., Lu, L., & Summers, R. M. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.3.036501 · Cited by 573
A review of approaches to identifying patient phenotype cohorts using electronic health records
Shivade, C., Raghavan, P., Fosler‐Lussier, E., Embí, P. J., Elhadad, N., Johnson, S. B., & Lai, A. M. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001935 · Cited by 549
Image Reconstruction is a New Frontier of Machine Learning
Wang, G., Ye, J., Mueller, K. D., & Fessler, J. A. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2833635 · Cited by 527
Self-citationNewton, K. M., Peissig, P. L., Kho, A. N., Bielinski, S. J., Berg, R. L., Choudhary, V., Basford, M., Chute, C. G., Kullo, I. J., Li, R., et al. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-000896 · Cited by 415
PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability
Kirby, J., Speltz, P., Rasmussen, L. V., Basford, M., Gottesman, O., Peissig, P. L., Pacheco, J. A., Tromp, G., Pathak, J., Carrell, D., et al. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv202 · Cited by 371
Zeng, Q. T., Goryachev, S., Weiss, S., Sordo, M., Murphy, S. N., & Lazarus, R. (2006) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-6-30 · Cited by 360
Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning
Weston, A. D., Korfiatis, P., Kline, T. L., Philbrick, K. A., Kostandy, P., Sakinis, T., Sugimoto, M., Takahashi, N., & Erickson, B. J. (2019) · Radiology · doi:10.1148/radiol.2018181432 · Cited by 358
Self-citationDeep learning enables reduced gadolinium dose for contrast-enhanced brain MRI
Gong, E., Pauly, J. M., Wintermark, M., & Zaharchuk, G. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25970 · Cited by 351
Distributed deep learning networks among institutions for medical imaging
Chang, K., Balachandar, N., Lam, C., Yi, D., Brown, J., Beers, A., Rosen, B., Rubin, D. L., & Kalpathy-Cramer, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy017 · Cited by 340
Self-citationElectronic Medical Records for Genetic Research: Results of the eMERGE Consortium
Kho, A. N., Pacheco, J. A., Peissig, P. L., Rasmussen, L., Newton, K. M., Weston, N., Crane, P. K., Pathak, J., Chute, C. G., Bielinski, S. J., et al. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3001807 · Cited by 336
Electronic medical records for discovery research in rheumatoid arthritis
Liao, K. P., Cai, T., Gainer, V. S., Goryachev, S. D., Zeng‐Treitler, Q., Raychaudhuri, S., Peter Szolovits, B., Churchill, S. E., Murphy, S. N., Kohane, I. S., et al. (2010) · Arthritis Care & Research · doi:10.1002/acr.20184 · Cited by 319
Banerjee, I., Yuan, L., Chen, M. C., Hasan, S. A., Langlotz, C. P., Moradzadeh, N., Chapman, B. E., Amrhein, T. J., Mong, D. A., Rubin, D. L., et al. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.11.004 · Cited by 292
Self-citationMaking data sharing work: The FCP/INDI experience
Mennes, M., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.064 · Cited by 280
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
Ultra–Low-Dose18F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs
Chen, K. T., Gong, E., Macruz, F., Xu, J., Boumis, A., Khalighi, M. M., Poston, K. L., Sha, S. J., Greicius, M. D., Mormino, E. C., et al. (2018) · Radiology · doi:10.1148/radiol.2018180940 · Cited by 251
Toward Best Practices in Radiology Reporting
Kahn, C. E., Langlotz, C. P., Burnside, E. S., Carrino, J. A., Channin, D. S., Hovsepian, D. M., & Rubin, D. L. (2009) · Radiology · doi:10.1148/radiol.2523081992 · Cited by 245
Self-citationWei, W.-Q., Teixeira, P. L., Mo, H., Cronin, R. M., Warner, J. L., & Denny, J. C. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv130 · Cited by 230
Deep Learning to Classify Radiology Free-Text Reports
Chen, M. C., Ball, R. L., Yang, L., Moradzadeh, N., Chapman, B. E., Larson, D. B., Langlotz, C. P., Amrhein, T. J., & Lungren, M. P. (2018) · Radiology · doi:10.1148/radiol.2017171115 · Cited by 217
Self-citationAssessment 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
Self-citationRADPEER quality assurance program: a multifacility study of interpretive disagreement rates
Borgstede, J. P., Lewis, R. S., Bhargavan, M., & Sunshine, J. H. (2004) · Journal of the American College of Radiology · doi:10.1016/s1546-1440(03)00002-4 · Cited by 203
Hripcsak, G. M., Austin, J. H. M., Alderson, P. O., & Friedman, C. (2002) · Radiology · doi:10.1148/radiol.2241011118 · Cited by 201
Information extraction from multi-institutional radiology reports
Hassanpour, S., & Langlotz, C. P. (2016) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2015.09.007 · Cited by 182
Self-citationAccuracy of Diagnostic Procedures: Has It Improved Over the Past Five Decades?
Berlin, L. (2007) · American Journal of Roentgenology · doi:10.2214/ajr.06.1270 · Cited by 165
Chen, K. T., Gong, E., Macruz, F., Xu, J., Boumis, A., Khalighi, M. M., Poston, K. L., Sha, S. J., Greicius, M. D., Mormino, E. C., et al. (2020) · Radiology · doi:10.1148/radiol.2020202527 · Cited by 152
Electronic medical record phenotyping using the anchor and learn framework
Halpern, Y., Horng, S., Choi, Y., & Sontag, D. A. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw011 · Cited by 151
Learning statistical models of phenotypes using noisy labeled training data
Agarwal, V., Podchiyska, T., Banda, J. M., Goel, V. V., Leung, T. I., Minty, E. P., Sweeney, T. E., Ross, E., & Shah, N. H. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw028 · Cited by 141
Sistrom, C. L., Dreyer, K. J., Dang, P. A., Weilburg, J. B., Boland, G. W., Rosenthal, D. I., & Thrall, J. H. (2009) · Radiology · doi:10.1148/radiol.2532090200 · Cited by 136
Dreyer, K. J., Kalra, M. K., Maher, M. M., Hurier, A. M., Asfaw, B. A., Schultz, T., Halpern, E. F., & Thrall, J. H. (2005) · Radiology · doi:10.1148/radiol.2341040049 · Cited by 122
ISMRM Raw data format: A proposed standard for MRI raw datasets
Inati, S., Naegele, J., Zwart, N. R., Roopchansingh, V., Lizak, M. J., Hansen, D. C., Liu, C., Atkinson, D., Kellman, P., Kozerke, S., et al. (2016) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26089 · Cited by 120
TCIA: An information resource to enable open science
Prior, F., Clark, K., Commean, P. K., Freymann, J., Jaffe, C. C., Kirby, J., Moore, S., Smith, K., Tarbox, L., Vendt, B. A., et al. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6609742 · Cited by 114
Richesson, R., Sun, J., Pathak, J., Kho, A., & Denny, J. C. (2016) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2016.05.005 · Cited by 110
Augmented Reality Simulator for Training in Two-Dimensional Echocardiography
Weidenbach, M., Wick, C., Pieper, S., Quast, K. J., Fox, T. J., Grunst, G., & Redel, D. A. (2000) · Computers and Biomedical Research · doi:10.1006/cbmr.1999.1527 · Cited by 102
Common Data Elements in Radiology
Rubin, D. L., & Kahn, C. E. (2016) · Radiology · doi:10.1148/radiol.2016161553 · Cited by 97
Banerjee, I., Chen, M. C., Lungren, M. P., & Rubin, D. L. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.11.012 · Cited by 82
Self-citationA text processing pipeline to extract recommendations from radiology reports
Yetisgen-Yildiz, M., Gunn, M. L. D., Xia, F., & Payne, T. H. (2013) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2012.12.005 · Cited by 79
Adaptive local window for level set segmentation of CT and MRI liver lesions
Hoogi, A., Beaulieu, C. F., Cunha, G. M., Heba, E. R., Sirlin, C. B., Napel, S. A., & Rubin, D. L. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.01.002 · Cited by 79
Kuperman, G. J., Friedman, C., & Hripcsak, G. M. (1998) · Methods of Information in Medicine · doi:10.1055/s-0038-1634499 · Cited by 78
Ratner, A., Bach, S. H., Ehrenberg, H. R., & Ré, C. (2017) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3035918.3056442 · Cited by 77
Banda, J. M., Halpern, Y., Sontag, D. A., & Shah, N. H. (2017) · PubMed Central · Cited by 67
Overby, C. L., Pathak, J., Gottesman, O., Haerian, K., Perotte, R., Murphy, S. P., Bruce, K., Johnson, S., Talwalkar, J. A., Shen, Y., et al. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001930 · Cited by 66
Relational machine learning for electronic health record-driven phenotyping
Peissig, P. L., Costa, V. S., Caldwell, M. D., Rottscheit, C., Berg, R. L., Mendonça, E. A., & Page, D. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.07.007 · Cited by 62
Rasmussen, L. V., Thompson, W. K., Pacheco, J. A., Kho, A., Carrell, D., Pathak, J., Peissig, P. L., Tromp, G., Denny, J. C., & Starren, J. B. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.06.007 · Cited by 59
Elkins, J. S., Friedman, C., Boden-Albala, B. M., Sacco, R. L., & Hripcsak, G. M. (2000) · Computers and Biomedical Research · doi:10.1006/cbmr.1999.1535 · Cited by 57
Automated Detection of Critical Results in Radiology Reports
Lakhani, P. C., Kim, W., & Langlotz, C. P. (2011) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-011-9426-6 · Cited by 55
Self-citationThe Effect of Verification on the Assessment of Imaging Techniques
Révész, G., Kundel, H. L., & Bonitatibus, M. (1990) · Investigative Radiology · doi:10.1097/00004424-199004000-00024 · Cited by 48
Image Sharing: Evolving Solutions in the Age of Interoperability
Mendelson, D. S., Erickson, B. J., & Choy, G. (2014) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2014.09.013 · Cited by 45
Self-citationGeneral Bounds on the Number of Examples Needed for Learning Probabilistic Concepts
Simon, H. U. (1996) · Journal of Computer and System Sciences · doi:10.1006/jcss.1996.0019 · Cited by 29
Lakhani, P. C., & Langlotz, C. P. (2009) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-009-9237-1 · Cited by 27
Self-citationJackson, S. L., Frederick, P. D., PEPE, M. S., Nelson, H. D., Weaver, D. L., Allison, K. H., Carney, P. A., Geller, B. M., Tosteson, A. N. A., Onega, T., et al. (2016) · Annals of Surgical Oncology · doi:10.1245/s10434-016-5695-0 · Cited by 26
Sheller, M., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.04304 · Cited by 20
Pediatric echocardiograms performed at primary centers: Diagnostic errors and missing links!
Shah, S., Saraf, R., Suresh, P., & Maheshwari, S. (2015) · Annals of Pediatric Cardiology · doi:10.4103/0974-2069.149514 · Cited by 16
Identifying genetically driven clinical phenotypes using linear mixed models
Mosley, J. D., Witte, J. S., Larkin, E. K., Bastarache, L. A., Shaffer, C. M., Karnes, J. H., Stein, C. M., Phillips, E. J., Hebbring, S. J., Brilliant, M. H., et al. (2016) · Nature Communications · doi:10.1038/ncomms11433 · Cited by 16
Phenotyping Adverse Drug Reactions: Statin-Related Myotoxicity.
Wiley, L. K., Moretz, J. D., Denny, J. C., Peterson, J. F., & Bush, W. S. (2015) · Europe PMC (PubMed Central) · Cited by 14
(2001) · doi:10.1002/pds.4850 · Cited by 7
Flagged first, then by citations
Research works printed but not linked: 6
- National Academy of Medicine. Improving Diagnosis in Health Care. Washington, DC: The National Academies Press, 2015.
- Prior FW, 2013, Conf Proc IEEE Eng Med Biol Soc, 2013, 1282
- Yan K, 2018, J Med Imaging (Bellingham), 5, 036501
- Banda JM, 2017, AMIA Jt Summits Transl Sci Proc, 2017, 48
- Wiley LK, 2015, AMIA Jt Summits Transl Sci Proc, 2015, 466
- Aronson AR, 2001, Proc AMIA Symp, 17
Cited by
Sorted by citations
The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 4,015
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524
Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020) · Database · doi:10.1093/database/baaa010 · Cited by 1,035
Preparing Medical Imaging Data for Machine Learning
Willemink, M. J., Koszek, W. A., Hardell, C., Wu, J., Fleischmann, D., Harvey, H., Folio, L. R., Summers, R. M., Rubin, D. L., & Lungren, M. P. (2020) · Radiology · doi:10.1148/radiol.2020192224 · Cited by 1,020
Self-citationYang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717
Metadata
- Authors
- Langlotz, C. P., Allen, B., Erickson, B. J., Kalpathy-Cramer, J., Bigelow, K., Cook, T. S., Flanders, A. E., Lungren, M. P., Mendelson, D. S., Rudie, J. D., Wang, G., & Kandarpa, K.
- Affiliations
- Mayo Clinic (US)
- National Institutes of Health (US)
- ORCID
- Venue
- Radiology
- Published
- 2019-06
- Volume
- 291
- Issue
- 3
- Pages
- 781-791
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC6542624
- OpenAlex
- W2939071985
- Cited by
- 368
- References
- 85
- Citations by year
- 2026: 34 · 2025: 33 · 2024: 45 · 2023: 57 · 2022: 56 · 2021: 68 · 2020: 58 · 2019: 17
- FWCI
- 15.77
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- artificial intelligence in medical imaging, research priorities, image reconstruction, image segmentation, computer-aided detection, computer-aided classification, radiogenomics, automatic image annotation, electronic phenotyping, federated learning, pre-trained models, explainable AI
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Diagnostic Imaging, Humans, Image Interpretation, Computer-Assisted, Biomedical Research
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
