Artificial Intelligence in Radiology: Opportunities and Challenges

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works

Radiomics: Images Are More than Pictures, They Are Data

Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016) · Radiology · doi:10.1148/radiol.2015151169 · Cited by 8,421 · see citing works

MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports

Johnson, A. E. W., Pollard, T. J., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-Y., Mark, R. G., & Horng, S. (2019) · Scientific Data · doi:10.1038/s41597-019-0322-0 · Cited by 1,816 · see citing works

Why People Fail to Recognize Their Own Incompetence

Dunning, D., Johnson, K., Ehrlinger, J., & Kruger, J. (2003) · Current Directions in Psychological Science · doi:10.1111/1467-8721.01235 · Cited by 1,224 · see citing works

Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations

Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021) · Nature Medicine · doi:10.1038/s41591-021-01595-0 · Cited by 928 · see citing works

Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis

Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1919012117 · Cited by 685 · see citing works

AI for radiographic COVID-19 detection selects shortcuts over signal

DeGrave, A. J., Janizek, J. D., & Lee, S.-I. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00338-7 · Cited by 631 · see citing works

Medical Imaging 2015: Computer-Aided Diagnosis

Hadjiiski, L. M., & Tourassi, G. D. (2015) · SPIE eBooks · Cited by 601 · see citing works

Natural language processing in radiology: a systematic review

Pons, E., Braun, L. M. M., Hunink, M. G. M., & Kors, J. A. (2016) · Radiology · doi:10.1148/radiol.16142770 · Cited by 573 · see citing works

Towards a standard for identifying and managing bias in artificial intelligence

Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022) · doi:10.6028/nist.sp.1270 · Cited by 548 · see citing works

Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration

Arbabshirani, M. R., Fornwalt, B. K., Mongelluzzo, G. J., Suever, J. D., Geise, B. D., Patel, A. A., & Moore, G. J. (2018) · npj Digital Medicine · doi:10.1038/s41746-017-0015-z · Cited by 443 · see citing works

Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms

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

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301590 · Cited by 392 · see citing works

Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance

Dratsch, 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

Radimagenet: an open Radiologic deep learning research Dataset for effective transfer learning

Mei, X., Liu, Z., Robson, P. M., Marinelli, B., Huang, M., Doshi, A., Jacobi, A., Cao, C., Link, K. E., Yang, T., et al. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210315 · Cited by 341 · see citing works

Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors

Strohm, L., Hehakaya, C., Ranschaert, E. R., Boon, W. P. C., & Moors, E. H. M. (2020) · European Radiology · doi:10.1007/s00330-020-06946-y · Cited by 329 · see citing works

Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma

Peng, H., Dong, D., Fang, M.-J., Li, L., Tang, L., Chen, L., Li, W., Mao, Y., Fan, W., Liu, L., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-3065 · Cited by 327 · see citing works

Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features

Gevaert, O., Mitchell, L. A., Achrol, A. S., Xu, J., Echegaray, S., Steinberg, G. K., Cheshier, S. H., Napel, S., Zaharchuk, G., & Plevritis, S. K. (2014) · Radiology · doi:10.1148/radiol.14131731 · Cited by 319 · see citing works

Clinical Natural Language Processing in languages other than English: opportunities and challenges

Aurélie Névéol, A., Dalianis, H., Velupillai, S. U., Savova, G., & Zweigenbaum, P. (2018) · Journal of Biomedical Semantics · doi:10.1186/s13326-018-0179-8 · Cited by 313 · see citing works

Radiogenomics of Glioblastoma: Machine Learning–based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features

Kickingereder, P., Bonekamp, D., Nowosielski, M., Kratz, A., Sill, M., Burth, S., Wick, A., Eidel, O., Schlemmer, H., Radbruch, A., et al. (2016) · Radiology · doi:10.1148/radiol.2016161382 · Cited by 304 · see citing works

Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks

Annarumma, M., Withey, S. J., Bakewell, R. J., Pesce, E., Goh, V., & Montana, G. (2019) · Radiology · doi:10.1148/radiol.2018180921 · Cited by 303 · see citing works

Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR

Trebeschi, S., van Griethuysen, J. J. M., Lambregts, D. M. J., Lahaye, M. J., Parmar, C., Bakers, F. C. H., Peters, N. H. G. M., Beets‐Tan, R. G. H., & Aerts, H. J. W. L. (2017) · Scientific Reports · doi:10.1038/s41598-017-05728-9 · Cited by 293 · 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

Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE)

Kelly, B. S., Judge, C., Bollard, S. M., Clifford, S. M., Healy, G. M., Aziz, A., Mathur, P., Islam, S., Yeom, K. W., Lawlor, A., et al. (2022) · European Radiology · doi:10.1007/s00330-022-08784-6 · Cited by 252 · see citing works

Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging

Mazurowski, M. A., Zhang, J., Grimm, L. J., Yoon, S. C., & Silber, J. I. (2014) · Radiology · doi:10.1148/radiol.14132641 · Cited by 241 · see citing works

Disparities in Lung Cancer Screening: A Review

Haddad, D. N., Sandler, K. L., Henderson, L. M., Rivera, M. P., & Aldrich, M. C. (2020) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201907-556cme · Cited by 212 · see citing works

Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra–Low-Dose Chest CT

Jiang, B., Li, N., Shi, X., Zhang, S., Li, J., de Bock, G. H., Vliegenthart, R., & Xie, X. (2022) · Radiology · doi:10.1148/radiol.210551 · Cited by 207 · see citing works

Artificial intelligence for precision education in radiology

Duong, M. T., Rauschecker, A. M., Rudie, J. D., Chen, P.-H., Cook, T. S., Bryan, R. N., & Mohan, S. (2019) · The British Journal of Radiology · doi:10.1259/bjr.20190389 · Cited by 205 · see citing works

Assessing the accuracy of a deep learning method to risk stratify indeterminate pulmonary nodules

Massion, P. P., Antic, S., Ather, S., Arteta, C., Brabec, J., Chen, H., Declerck, J., Dufek, D., Hickes, W., Kadir, T., et al. (2020) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201903-0505oc · Cited by 190 · see citing works

To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)

Omoumi, P., Ducarouge, A., Tournier, A., Harvey, H., Kahn, C. E., Verchère, F. L.-D., dos Santos, D. P., Kober, T., & Richiardi, J. (2021) · European Radiology · doi:10.1007/s00330-020-07684-x · Cited by 171 · see citing works

Computerized Bone Age Estimation Using Deep Learning Based Program: Evaluation of the Accuracy and Efficiency

Kim, J. R., Shim, W. H., Yoon, H. M., Hong, S. H., Lee, J. S., Cho, Y. A., & Kim, S. (2017) · American Journal of Roentgenology · doi:10.2214/ajr.17.18224 · Cited by 168 · see citing works

Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer

Meng, X., Xia, W., Xie, P., Zhang, R., Li, W., Wang, M., Xiong, F., Liu, Y., Fan, X., Xie, Y., et al. (2018) · European Radiology · doi:10.1007/s00330-018-5763-x · Cited by 157 · see citing works

Fast Enhanced CT Metal Artifact Reduction Using Data Domain Deep Learning

Ghani, M. U., & Karl, W. C. (2019) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2019.2937221 · Cited by 151 · see citing works

Artificial Intelligence in Radiology—Ethical Considerations

Brady, A. P., & Neri, E. (2020) · Diagnostics · doi:10.3390/diagnostics10040231 · Cited by 150 · see citing works

Mitigating Bias in Radiology Machine Learning: 1. Data Handling

Rouzrokh, P., Khosravi, B., Faghani, S., Moassefi, M., Vera Garcia, D. V., Singh, Y., Zhang, K., Conte, G. M., & Erickson, B. J. (2022) · Radiology: Artificial Intelligence · doi:10.1148/ryai.210290 · Cited by 145 · see citing works

An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education

Huisman, M., Ranschaert, E. R., Parker, W., Mastrodicasa, D., Kočí, M., dos Santos, D. P., Coppola, F., Morozov, S., Zins, M., Bohyn, C., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07782-4 · Cited by 140 · 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

Disparities in screening mammography services by race/ethnicity and health insurance

Rauscher, G. H., Allgood, K. L., Whitman, S., & Conant, E. (2012) · Journal of Women s Health · doi:10.1089/jwh.2010.2415 · Cited by 128 · see citing works

Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement

Geis, J. R., Brady, A. P., Wu, C. C., Spencer, J., Ranschaert, E. R., Jaremko, J. L., Langer, S. G., Kitts, A. B., Birch, J. V., Shields, W. F., et al. (2019) · Insights into Imaging · doi:10.1186/s13244-019-0785-8 · Cited by 121 · see citing works

Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson’s Natural Language Processing Algorithm

Trivedi, H., Mesterhazy, J., Laguna, B. A., Vu, T.-K. H., & Sohn, J. H. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-0021-3 · Cited by 110 · see citing works

Optimization of Radiology Workflow with Artificial Intelligence

Ranschaert, E. R., Topff, L., & Pianykh, O. S. (2021) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2021.06.006 · Cited by 103 · see citing works

Active Reprioritization of the Reading Worklist Using Artificial Intelligence Has a Beneficial Effect on the Turnaround Time for Interpretation of Head CT with Intracranial Hemorrhage

O’Neill, T. J., Xi, Y., Stehel, E., Browning, T. G., Ng, Y. S., Baker, C., & Peshock, R. M. (2020) · Radiology Artificial Intelligence · doi:10.1148/ryai.2020200024 · Cited by 98 · see citing works

Machine Learning for Automation of Radiology Protocols for Quality and Efficiency Improvement

Kalra, A., Chakraborty, A. K., Fine, B., & Reicher, J. J. (2020) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.03.012 · Cited by 94 · see citing works

Federated Learning in Medical Imaging: Part I: Toward Multicentral Health Care Ecosystems

Darzidehkalani, E., Ghasemi-Rad, M., & van Ooijen, P. M. A. (2022) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2022.03.015 · Cited by 94 · see citing works

Integrating Al Algorithms into the Clinical Workflow

Juluru, K., Shih, H.-H., Murthy, K. N. K., Elnajjar, P., El-Rowmeim, A., Roth, C. J., Genereaux, B. W., Fox, J. J., Siegel, E. L., & Rubin, D. L. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021210013 · Cited by 85 · see citing works

Artificial Intelligence Tool for Detection and Worklist Prioritization Reduces Time to Diagnosis of Incidental Pulmonary Embolism at CT

Topff, L., Ranschaert, E. R., Rutten, A., Negoita, A. N., de Menezes, R. X., Beets‐Tan, R. G. H., & Visser, J. J. (2023) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.220163 · Cited by 84 · see citing works

Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer

Meng, Y., Zhang, Y., Dong, D., Li, C., Liang, X., Zhang, C., Wan, L., Zhao, X., Xu, K., Zhou, C., et al. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25968 · Cited by 73 · see citing works

Artificial intelligence in radiology: friend or foe? Where are we now and where are we heading?

Pakdemirli, E. (2019) · Acta Radiologica Open · doi:10.1177/2058460119830222 · Cited by 72 · see citing works

Use of Machine Learning to Identify Follow-Up Recommendations in Radiology Reports

Carrodeguas, E., Lacson, R. C., Swanson, W., & Khorasani, R. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2018.10.020 · Cited by 69 · see citing works

Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools

Kapoor, N., Lacson, R. C., & Khorasani, R. (2020) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.08.016 · Cited by 66 · see citing works

Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

Kalantar, R., Lin, G., Winfield, J. M., Messiou, C., Lalondrelle, S., Blackledge, M., & Koh, D. (2021) · Diagnostics · doi:10.3390/diagnostics11111964 · Cited by 66 · see citing works

Machine learning based natural language processing of radiology reports in orthopaedic trauma

Olthof, A. W., Shouche, P., Fennema, E. M., IJpma, F. F. A., Koolstra, R. H. C., Stirler, V. M. A., van Ooijen, P. M. A., & Cornelissen, L. J. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106304 · Cited by 65 · see citing works

Translating AI to Clinical Practice: Overcoming Data Shift with Explainability

Choi, Y., Yu, W., Nagarajan, M. B., Teng, P., Goldin, J. G., Raman, S. S., Enzmann, D. R., Kim, G. H. J., & Brown, M. S. (2023) · RadioGraphics · doi:10.1148/rg.220105 · Cited by 65 · see citing works

A Natural Language Processing-based Model to Automate MRI Brain Protocol Selection and Prioritization

Brown, A. D., & Marotta, T. R. (2016) · Academic Radiology · doi:10.1016/j.acra.2016.09.013 · Cited by 64 · see citing works

Correction of Motion Artifacts Using a Multiscale Fully Convolutional Neural Network

Sommer, K., Saalbach, A., Brosch, T., Hall, C. S., Cross, N. M., & Andre, J. B. (2020) · American Journal of Neuroradiology · doi:10.3174/ajnr.a6436 · Cited by 54 · see citing works

Thoracic Radiologists’ Versus Computer Scientists’ Perspectives on the Future of Artificial Intelligence in Radiology

Eltorai, A. E. M., Bratt, A. K., & Guo, H. H. (2019) · Journal of Thoracic Imaging · doi:10.1097/rti.0000000000000453 · Cited by 51 · see citing works

Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning–based filter using convolutional neural network

Kromrey, M., Tamada, D., Johno, H., Funayama, S., Nagata, N., Ichikawa, S., Kühn, J., Onishi, H., & Motosugi, U. (2020) · European Radiology · doi:10.1007/s00330-020-07006-1 · Cited by 48 · see citing works

Prognostic value of multiparametric MRI-based radiomics model: Potential role for chemotherapeutic benefits in locally advanced rectal cancer

Cui, Y., Yang, W., Ren, J., Li, D., Du, X., Zhang, J., & Yang, X. (2020) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2020.09.039 · Cited by 47 · see citing works

The Evolving Importance of Artificial Intelligence and Radiology in Medical Trainee Education

Fischetti, C., Bhatter, P., Frisch, E., Sidhu, A., Helmy, M., Lungren, M., & Duhaime, E. (2022) · Academic Radiology · doi:10.1016/j.acra.2021.03.023 · Cited by 47 · see citing works

Radiomics modelling in rectal cancer to predict disease-free survival: evaluation of different approaches

Tibermacine, H., Rouanet, P., Sbarra, M., Forghani, R., Reinhold, C., Nougaret, S., the GRECCAR Study Group, Rullier, É., Lelong, B., Maingon, P., et al. (2021) · British journal of surgery · doi:10.1093/bjs/znab191 · Cited by 45 · see citing works

Is Artificial Intelligence Replacing Our Radiology Stars? Not Yet!

Cacciamani, G. E., Sanford, D. I., Chu, T. N., Kaneko, M., Abreu, A. L., Duddalwar, V. A., & Gill, I. S. (2022) · European Urology Open Science · doi:10.1016/j.euros.2022.09.024 · Cited by 43 · see citing works

A Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations

Sreekumari, A. B., Shanbhag, D., Yeo, D. T. B., Foo, T., Pilitsis, J. G., Polzin, J. A., Patil, U., Coblentz, A. C., Kapadia, A. N., Khinda, J., et al. (2019) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5926 · Cited by 42 · see citing works

Extracting clinical terms from radiology reports with deep learning

Sugimoto, K., Takeda, T., Oh, J., Wada, S., Konishi, S., Yamahata, A., Manabe, S., Tomiyama, N., Matsunaga, T., Nakanishi, K., et al. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103729 · Cited by 41 · see citing works

The reporting quality of natural language processing studies: systematic review of studies of radiology reports

Davidson, E. M., Poon, M. T. C., Casey, A., Grivas, A., Duma, D., Dong, H., Suárez-Paniagua, V., Grover, C., Tobin, R., Whalley, H. C., et al. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00671-8 · Cited by 40 · see citing works

Performance of an artificial intelligence tool with real-time clinical workflow integration – Detection of intracranial hemorrhage and pulmonary embolism

Buls, N., Watté, N., Nieboer, K., Ilsen, B., & Mey, J. D. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.015 · Cited by 39 · see citing works

Medical Image Resource Center?making electronic teaching files from PACS

Lim, C. C. T., Yang, G. L., Nowinski, W. L., & Hui, F. (2003) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-003-1660-0 · Cited by 35 · see citing works

Applying Artificial Intelligence to Mitigate Effects of Patient Motion or Other Complicating Factors on Image Quality

Nguyen, X. V., Öztek, M. A., Nelakurti, D. D., Brunnquell, C. L., Mossa‐Basha, M., Haynor, D. R., & Prevedello, L. M. S. (2020) · Topics in Magnetic Resonance Imaging · doi:10.1097/rmr.0000000000000249 · Cited by 35 · see citing works

Automated Identification and Measurement Extraction of Pancreatic Cystic Lesions from Free-Text Radiology Reports Using Natural Language Processing

Yamashita, R., Bird, K. N., Cheung, P. Y.-C., Decker, J. H., Flory, M., Goff, D., Morimoto, L. N., Shon, A., Wentland, A. L., Rubin, D. L., et al. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.210092 · Cited by 34 · see citing works

Challenges of Radiology education in the era of artificial intelligence

Gorospe‐Sarasúa, L., Muñoz-Olmedo, J. M., Sendra‐Portero, F., & de Luis-Garcı́a, R. (2022) · Radiología (English Edition) · doi:10.1016/j.rxeng.2020.10.012 · Cited by 32 · see citing works

Racial Disparities in Lung Cancer Screening Among Veterans, 2013 to 2021

Navuluri, N., Morrison, S., Green, C. L., Woolson, S., Riley, I. L., Cox, C. E., Zullig, L. L., & Shofer, S. L. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.18795 · Cited by 29 · see citing works

Evaluating Artificial Intelligence Systems to Guide Purchasing Decisions

Filice, R. W., Mongan, J., & Kohli, M. D. (2020) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.09.045 · Cited by 27 · see citing works

Domain specific word embeddings for natural language processing in radiology

Chen, T. L., Emerling, M., Chaudhari, G., Chillakuru, Y. R., Seo, Y., Vu, T.-K. H., & Sohn, J. H. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103665 · Cited by 27 · see citing works

The Importance of Imaging Informatics and Informaticists in the Implementation of AI

Cook, T. S. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.10.002 · Cited by 25 · see citing works

The role of federated learning models in medical imaging

Kwak, L., & Bai, H. (2023) · Radiology Artificial Intelligence · doi:10.1148/ryai.230136 · Cited by 25 · see citing works

Deep-Learning Language-Modeling Approach for Automated, Personalized, and Iterative Radiology-Pathology Correlation

Filice, R. W. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.05.007 · Cited by 24 · see citing works

Radiology-Pathology Correlation to Facilitate Peer Learning: An Overview Including Recent Artificial Intelligence Methods

Filice, R. W. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.05.010 · Cited by 22 · see citing works

Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation

Barinov, L., Jairaj, A., Middleton, W. D., Michael, D. L., Beland, M. D., Kirsch, J., Filice, R. W., Reverter, J. L., Argüelles, I., & Grant, E. G. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00884-z · Cited by 22 · see citing works

Is AI the way forward for reducing metal artifacts in CT? Development of a generic deep learning-based method and initial evaluation in patients with sacroiliac joint implants

Selles, M., Slotman, D. J., van Osch, J. A. C., Nijholt, I. M., Wellenberg, R. H. H., Maas, M. M., & Boomsma, M. F. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110844 · Cited by 21 · see citing works

AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans

Wang, Y.-R., Qu, L., Sheybani, N., Luo, X., Wang, J., Hawk, K. E., Theruvath, A. J., Gatidis, S., Xiao, X., Pribnow, A. K., et al. (2023) · Radiology Artificial Intelligence · doi:10.1148/ryai.220246 · Cited by 21 · see citing works

PathBot: A Radiology-Pathology Correlation Dashboard

Kelahan, L. C., Kalaria, A. D., & Filice, R. W. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-9969-2 · Cited by 18 · see citing works

Multi-Center Follow-up Study to Develop a Classification System Which Differentiates Mucinous Cystic Neoplasm of the Liver and Benign Hepatic Cyst Using Machine Learning

Hardie, A. D., Chamberlin, J., Boyum, J. H., Sharbidre, K., Petrocelli, R. D., Flemming, B. P., Zahid, M., Venkatesh, S. K., Mruthyunjayappa, S., Hajdu, C. H., et al. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.08.025 · Cited by 16 · see citing works

Developing a More Responsive Radiology Resident Dashboard

Chen, H., Gangaram, V., & Shih, G. (2018) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-018-0123-6 · Cited by 15 · see citing works

Moving Artificial Intelligence from Feasible to Real: Time to Drill for Gas and Build Roads

Chang, P. J. (2019) · Radiology · doi:10.1148/radiol.2019192527 · Cited by 14 · see citing works

Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: an investigation using the Lung Image Database Consortium dataset

Hancock, M. C., & Magnan, J. F. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2254446 · Cited by 14 · see citing works

Call Case Dashboard: Tracking R1 Exposure to High-Acuity Cases Using Natural Language Processing

Kelahan, L. C., Fong, A., Ratwani, R. M., & Filice, R. W. (2016) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2016.03.012 · Cited by 11 · see citing works

The NCI Imaging Data Commons as a platform for reproducible research in computational pathology

Schacherer, D. P., Herrmann, M. D., Clunie, D., Höfener, H., Clifford, W., Longabaugh, W. J. R., Pieper, S., Kikinis, R., Fedorov, A., & Homeyer, A. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107839 · Cited by 9 · see citing works

To buy or not to buy - Evaluating commercial AI solutions in radiology (the ECLAIR guidelines)

Omoumi, P. (2021) · doi:10.26226/morressier.615e2a8f7c09fc044a9739af · Cited by 7 · see citing works

RadImageNet: A Large-scale Radiologic Dataset for Enhancing Deep Learning Transfer Learning ResearchPreprint

Yang, Y., Mei, X., Robson, P. M., Marinelli, B. S., Huang, M., Doshi, A. H., Jacobi, A., Link, K. E., Yang, T. T., Cao, C., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-600803/v1 · Cited by 4 · see citing works

Principles and rationale of radiomics and radiogenomics

Napel, S. A. (2019) · Chapman & Hall/CRC eBooks · doi:10.1201/9781351208277-1 · Cited by 3 · see citing works

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects

Koçak, B., Ponsiglione, A., Stanzione, A., Bluethgen, C., Santinha, J., Ugga, L., Huisman, M., Klontzas, M. E., Cannella, R., & Cuocolo, R. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242854 · Cited by 168 · see citing works

Implementing Artificial Intelligence Algorithms in the Radiology Workflow: Challenges and Considerations

Korfiatis, P., Kline, T. L., Meyer, H. M., Khalid, S., Leiner, T., Loufek, B. T., Blezek, D., Vidal, D. E., Hartman, R. P., Joppa, L. J., et al. (2025) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2024.100188 · Cited by 46 · see citing works

AI in Dental Radiology—Improving the Efficiency of Reporting With ChatGPT: Comparative Study

Stephan, D., Bertsch, A., Burwinkel, M., Vinayahalingam, S., Al‐Nawas, B., Kämmerer, P. W., & Thiem, D. G. E. (2024) · Journal of Medical Internet Research · doi:10.2196/60684 · Cited by 37 · see citing works

Application of Artificial Intelligence in Cone-Beam Computed Tomography for Airway Analysis: A Narrative Review

Ismail, I. N., Subramaniam, P. K., Adam, K. B. C., & Ghazali, A. B. (2024) · Diagnostics · doi:10.3390/diagnostics14171917 · Cited by 21 · see citing works

The AI-extended professional self: user-centric AI integration into professional practice with exemplars from healthcare

Schneider‐Kamp, A., & Godono, A. (2025) · AI & Society · doi:10.1007/s00146-025-02319-5 · Cited by 16 · see citing works

Measuring the Environmental Impact of MRI and CT: A Life Cycle Assessment

Carver, D. E., Pruthi, S., Struk, O., Vigil-Garcia, M., Meijer, C., Gehrels, J., Omary, R. A., Scheel, J. R., & Thiel, C. L. (2025) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2025.09.030 · Cited by 14 · see citing works

An integrative nomogram based on MRI radiomics and clinical characteristics for prognosis prediction in cervical spinal cord Injury

Zhang, Z., Li, N., Ding, Y., & Cheng, H. (2024) · European Spine Journal · doi:10.1007/s00586-024-08609-8 · Cited by 11 · see citing works

Artificial Intelligence Applications in Musculoskeletal Imaging

Shariatnia, M., Bagherieh, S., Semnani, F., Rafiei, N., Avval, A. H., Ollivier, M. P., Musahl, V., & Pareek, A. (2025) · Current Reviews in Musculoskeletal Medicine · doi:10.1007/s12178-025-09997-0 · Cited by 10 · see citing works

Artificial intelligence in cancer care: revolutionizing diagnosis, treatment, and precision medicine amid emerging challenges and future opportunities

Selvaraj, C., Cho, W. C. S., Kulanthaivel, L., Alothaim, A. S., Vijayakumar, R., Jayaprakashvel, M., & Desai, D. (2025) · 3 Biotech · doi:10.1007/s13205-025-04518-9 · Cited by 8 · see citing works

Establishment and validation of a ResNet-based radiomics model for predicting prognosis in cervical spinal cord injury patients

Zhang, Z., Li, N., Ding, Y., Sun, H., & Cheng, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-94358-7 · Cited by 5 · see citing works

The limits of artificial intelligence: prospects and challenges in the clinical workplace

Schneider‐Kamp, A., & Askegaard, S. (2024) · Current Opinion in Epidemiology and Public Health · doi:10.1097/pxh.0000000000000046 · Cited by 5 · see citing works

Artificial intelligence (AI) and CT in abdominal imaging: image reconstruction and beyond

Pisuchpen, N., Rao, S. S., Noda, Y., Kongboonvijit, S., Rezaei, A., & Kambadakone, A. R. (2025) · Abdominal Radiology · doi:10.1007/s00261-025-05031-6 · Cited by 4 · see citing works

Automatic Lower-Limb Length Measurement Network (A3LMNet): A Hybrid Framework for Automated Lower-Limb Length Measurement in Orthopedic Diagnostics

Rhyou, S.-Y., Cho, Y. J., Yoo, J.-C., Hong, S., Bae, S., Bae, H., & Yu, M. (2025) · Electronics · doi:10.3390/electronics14010160 · Cited by 3 · see citing works

Radiologic Error as an Emergent Property of Complex Adaptive Systems: Implications for Diagnostic Safety and Governance

Karamoskos, P. (2026) · American Journal of Roentgenology · doi:10.2214/ajr.26.34634 · Cited by 2 · see citing works

Efficient Selection of Rare Pathology Samples from Unlabeled Medical Data via Deep Active Learning

Kafetzis, I. S., Hann, A., & Fragulis, G. F. (2025) · International Conference on Modern Circuits and Systems Technologies · doi:10.1109/mocast65744.2025.11083944 · Cited by 1 · see citing works

Inequity in imaging: Why it matters? A statement from the Equity, Diversity and Inclusion Subcommittee of the European Society of Radiology

Parkar, A. P., Offiah, A. C., Ene, M.-A., Gheonea, I.-A., & European Society of Radiology (ESR) (2025) · Insights into Imaging · doi:10.1186/s13244-025-02144-w · Cited by 1 · see citing works

Innovations in MSK Radiology: Harnessing the Power of AI

Lohiya, N., Ajmera, P., Desai, S., & Jain, V. K. (2025) · Apollo Medicine · doi:10.1177/09760016251407469 · Cited by 1 · see citing works

EffiRadNet: Lightweight and User-Friendly Open-Source EfficientNet-Based Model for Radiology Image Binary Classification Tasks

Saliba, T., Rotzinger, D. C., Ilanjian, G., & Fahrni, G. (2026) · F1000Research · doi:10.12688/f1000research.177499.1 · Cited by 1 · see citing works

Artificial intelligence in orthopaedics: Clinical decision support, medical imaging, surgical planning, and outcome prediction

Dwajan, A., PATRO, D. R., Agarwal, A., & Lalhmingmawii, M. (2026) · World Journal of Clinical Cases · doi:10.12998/wjcc.v14.i17.120192 · Cited by 1 · see citing works

Deep learning and machine learning in image-based hepatocellular carcinoma detection: a systematic review and meta-analysis

Wang, J., Bai, J., Wang, Y., Wang, C., & Deng, H. (2026) · Abdominal Radiology · doi:10.1007/s00261-026-05627-6 · Cited by 1 · see citing works

Beyond Pattern Recognition: Call for Functionally Aware AI for Anatomical Illustration

Kawalec, A., & Simka, M. (2026) · JMIR Medical Education · doi:10.2196/92700 · Cited by 1 · see citing works

Role of radiologist with the advent of artificial intelligence in medical imaging

Boregowdanapalya, A. (2024) · International Journal of Research in Medical Sciences · doi:10.18203/2320-6012.ijrms20244173

AI and Radiology: Bridging the Gap Between Peer Review, Education, and Value-Based Care

Hussain, M. A., & Wrenn, M. E. (2025) · Applied Radiology · doi:10.37549/ar-d-24-0029

The Role of AI in Enhancing Imaging Modalities

Karthik, Y., Sujithra, M., & Senthilkumar, B. (2025) · Springer eBooks · doi:10.1007/978-3-032-02963-8_3

Beyond Burnout

Inam, H., & Sharaf, K. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-2727-3.ch007

Interpretable Deep Learning for Musculoskeletal Radiograph Classification: Optimizing CNN Architectures with Explainable Insights

Refat, S. H., Rahaman, M. A., Arafat, Y., Alam, P., Ramit, S. S., & Rahman, M. S. (2026) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl67539.2026.11452107

Equity, diversity, and inclusion: variations in clinical and academic radiology

Kassanje, C., Wong, E. H. Y., Bamidele, J. O., & Offiah, A. C. (2026) · Insights into Imaging · doi:10.1186/s13244-026-02278-5

AI-Based Denoising for Simulated Dose Reduction in Pediatric Chest Radiography: A Prospective Multicenter Evaluation of Image Quality and Diagnostic Performance

Shakhreet, B. Z. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.04.003

Bridging Industry and Academia: Proceedings from the 2025 Academy Roundtable on AI Implementation in Medical Imaging

Towbin, A. J., Kim, W., Kottler, N. E., Burkett, B. J., Chapelin, F., Chow, J., Cutler, C. S., Derdeyn, C. P., Dhyani, M., Doo, F. X., et al. (2026) · Radiology Artificial Intelligence · doi:10.1148/ryai.250671

Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations

Recht, M. P., Dewey, M., Dreyer, K., Langlotz, C., Niessen, W., Prainsack, B., & Smith, J. J. (2020) · European Radiology · doi:10.1007/s00330-020-06672-5 · Cited by 268 · see citing works

AI for Radiology: A Primer Part I. From Idea to Algorithm

Tejani, A. S., Rauschecker, A. M., Kohli, M. D., & Mongan, J. (2025) · Radiology · doi:10.1148/radiol.250692 · Cited by 6 · see citing works

Intelligence artificielle en pratique radiologique : entre rêve et réalité

Chênes, C., Lokaj, B., & Schmid, J. (2026) · JMIG · doi:10.53738/revmed.2026.22.964.48486

Pitfalls and Best Practices in Evaluation of AI Algorithmic Biases in Radiology

Yi, P. H., Bachina, P., Bharti, B., Garin, S. P., Kanhere, A., Kulkarni, P., Li, D. K. B., Parekh, V. S., Santomartino, S. M., Moy, L. P., et al. (2025) · Radiology · doi:10.1148/radiol.241674 · Cited by 28 · see citing works

Artificial Intelligence in Radiology: Current Applications, Clinical Challenges, and Future Perspectives

Fareed, A., Eman, M., Ali, K., & Rasheed, I. (2026) · Pakistan Journal of Medical & Cardiological Review · doi:10.66021/pakmcr1694