Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond

An overview of clinical decision support systems: Benefits, risks, and strategies for success

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Deep Convolutional Neural Network for Inverse Problems in Imaging

Jin, K. H., McCann, M. T., Froustey, E., & Unser, M. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2713099 · Cited by 2,529 · see citing works

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 · see citing works

Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality

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Deep Learning: A Primer for Radiologists

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Generative Adversarial Networks for Noise Reduction in Low-Dose CT

Wolterink, J. M., Leiner, T., Viergever, M. A., & Išgum, I. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2708987 · Cited by 1,038 · see citing works

A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction

Kang, E., Min, J., & Ye, J. C. (2017) · Medical Physics · doi:10.1002/mp.12344 · Cited by 827 · see citing works

Addressing Overutilization in Medical Imaging

Hendee, W. R., Becker, G. J., Borgstede, J. P., Bosma, J. L., Casarella, W. J., Erickson, B. A., Maynard, C. D., Thrall, J. H., & Wallner, P. E. (2010) · Radiology · doi:10.1148/radiol.10100063 · Cited by 622 · see citing works

The age of intelligent machines

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Use of High-Risk Coronary Atherosclerotic Plaque Detection for Risk Stratification of Patients With Stable Chest Pain

Ferencik, M., Mayrhofer, T., Bittner, D. O., Emami, H., Puchner, S. B., Lu, M. T., Meyersohn, N. M., Ivanov, A. V., Adami, E. C., Patel, M. R., et al. (2018) · JAMA Cardiology · doi:10.1001/jamacardio.2017.4973 · Cited by 562 · 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

Deep 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 · see citing works

Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma

Lin, L., Dou, Q., Jin, Y., Zhou, G., Tang, Y.-Q., Chen, W.-L., Su, B.-A., Liu, F., Tao, C., Jiang, N., et al. (2019) · Radiology · doi:10.1148/radiol.2019182012 · Cited by 344 · see citing works

Analysis of Appropriateness of Outpatient CT and MRI Referred From Primary Care Clinics at an Academic Medical Center: How Critical Is the Need for Improved Decision Support?

Lehnert, B. E., & Bree, R. L. (2010) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2009.11.010 · 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

Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians

Lin, D. J., Johnson, P. M., Knöll, F., & Lui, Y. W. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27078 · Cited by 295 · see citing works

Effectiveness of Clinical Decision Support in Controlling Inappropriate Imaging

Blackmore, C. C., Mecklenburg, R. S., & Kaplan, G. S. (2011) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2010.07.009 · Cited by 274 · see citing works

Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising

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Machine Learning in Radiology: Applications Beyond Image Interpretation

Lakhani, P. C., Prater, A. B., Hutson, R. K., Andriole, K. P., Dreyer, K. J., Morey, J., Prevedello, L. M. S., Clark, T. J., Geis, J. R., Itri, J. N., et al. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.09.044 · Cited by 255 · see citing works

Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging

Prevedello, L. M. S., Erdal, B. S., Ryu, J., Little, K. J., Demirer, M., Qian, S., & White, R. (2017) · Radiology · doi:10.1148/radiol.2017162664 · Cited by 248 · see citing works

The influence of patient centering on CT dose and image noise

Toth, T., Ge, Z., & Daly, M. P. (2007) · Medical Physics · doi:10.1118/1.2748113 · Cited by 199 · see citing works

Artificial Intelligence in Nuclear Medicine

Nensa, F., Demircioğlu, A., & Rischpler, C. (2019) · Journal of Nuclear Medicine · doi:10.2967/jnumed.118.220590 · Cited by 197 · see citing works

PENet - A Scalable Deep-Learning Model for Automated Diagnosis of Pulmonary Embolism Using Volumetric CT Imaging

Huang, S.-C., Kothari, T., Banerjee, I., Chute, C., Ball, R. L., Borus, N., Huang, A., Patel, B. N., Rajpurkar, P., Irvin, J., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-020-0266-y · Cited by 192 · see citing works

Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study

Recht, M. P., Žbontar, J., Sodickson, D. K., Knöll, F., Yakubova, N., Sriram, A., Murrell, T., Defazio, A., Rabbat, M., Rybak, L., et al. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.20.23313 · Cited by 177 · see citing works

Automated detection of pulmonary embolism in CT pulmonary angiograms using an AI-powered algorithm

Weikert, T., Winkel, D. J., Bremerich, J., Stieltjes, B., Parmar, V., Sauter, A. W., & Sommer, G. (2020) · European Radiology · doi:10.1007/s00330-020-06998-0 · Cited by 165 · see citing works

Using Data Mining to Detect Health Care Fraud and Abuse: A Review of Literature

Joudaki, H., Rashidian, A., Bidgoli, B. M., Mahmoodi, M., Geraili, B., Nasiri, M., & Arab, M. (2014) · Global Journal of Health Science · doi:10.5539/gjhs.v7n1p194 · Cited by 164 · see citing works

Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study

Racine, D., Becce, F., Viry, A., Monnin, P., Thomsen, B., Verdun, F. R., & Rotzinger, D. C. (2020) · Physica Medica · doi:10.1016/j.ejmp.2020.06.004 · 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

Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage

Ginat, D. T. (2019) · Neuroradiology · doi:10.1007/s00234-019-02330-w · Cited by 110 · see citing works

Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers

Bizzo, B. C., de Almeida, R. R., Michalski, M. H., & Alkasab, T. K. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.06.010 · Cited by 100 · 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

Efficiency Improvement in a Busy Radiology Practice: Determination of Musculoskeletal Magnetic Resonance Imaging Protocol Using Deep-Learning Convolutional Neural Networks

Lee, Y. H. (2018) · Journal of Digital Imaging · doi:10.1007/s10278-018-0066-y · Cited by 97 · see citing works

Toward Augmented Radiologists

Tajmir, S., & Alkasab, T. K. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.03.007 · Cited by 94 · 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

Socioeconomic and Demographic Predictors of Missed Opportunities to Provide Advanced Imaging Services

Glover, M., Daye, D., Khalilzadeh, O., Pianykh, O. S., Rosenthal, D. I., Brink, J. A., & Flores, E. J. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.05.015 · Cited by 93 · see citing works

The Inappropriate Use of Imaging Studies: A Report of the 2004 Intersociety Conference

Dunnick, N. R., Applegate, K. E., & Arenson, R. L. (2005) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2004.12.008 · Cited by 91 · see citing works

Using machine learning for sequence-level automated MRI protocol selection in neuroradiology

Brown, A. D., & Marotta, T. R. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx125 · Cited by 91 · see citing works

Clinical Assessment of Deep Learning–based Super-Resolution for 3D Volumetric Brain MRI

Rudie, J. D., Gleason, T., Barkovich, M. J., Wilson, D. M., Shankaranarayanan, A., Zhang, T., Wang, L., Gong, E., Zaharchuk, G., & Villanueva-Meyer, J. E. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210059 · Cited by 91 · see citing works

Machine Learning for Predicting Patient Wait Times and Appointment Delays

Curtis, C., Liu, C., Bollerman, T. J., & Pianykh, O. S. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.08.021 · Cited by 90 · see citing works

Artificial Intelligence in Cardiology—A Narrative Review of Current Status

Koulaouzidis, G., Jadczyk, T., Iakovidis, D. K., Koulaouzidis, A., Bisnaire, M., & Charisopoulou, D. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11133910 · Cited by 90 · see citing works

Artificial Intelligence Predictive Analytics in the Management of Outpatient MRI Appointment No-Shows

Chong, L. R., Tsai, K. T., Lee, L. L., Foo, S. G., & Chang, P. C. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.19.22594 · Cited by 87 · see citing works

Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation

Baltruschat, I. M., Steinmeister, L., Nickisch, H., Saalbach, A., Graß, M., Adam, G., Knopp, T., & Ittrich, H. (2020) · European Radiology · doi:10.1007/s00330-020-07480-7 · Cited by 86 · see citing works

Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

Slart, R. H. J. A., Williams, M. A., Juárez‐Orozco, L. E., Rischpler, C., Dweck, M. R., Glaudemans, A. W. J. M., Gimelli, A., Georgoulias, P., Gheysens, O. S., Gaemperli, O., et al. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05341-z · Cited by 86 · see citing works

Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics

Guillon, L. L., Camirand, D., Guilbert, F., & Forghani, R. (2020) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2020.08.008 · Cited by 84 · see citing works

Evaluation of an AI-Based Detection Software for Acute Findings in Abdominal Computed Tomography Scans

Winkel, D. J., Heye, T. J., Weikert, T., Boll, D. T., & Stieltjes, B. (2018) · Investigative Radiology · doi:10.1097/rli.0000000000000509 · Cited by 83 · see citing works

Fully Automated Segmentation Algorithm for Hematoma Volumetric Analysis in Spontaneous Intracerebral Hemorrhage

Ironside, N. T., Chen, C., Mutasa, S., Sim, J., Marfatia, S., Roh, D. J., Ding, D., Mayer, S. A., Lignelli, A., & Connolly, E. S. (2019) · Stroke · doi:10.1161/strokeaha.119.026561 · Cited by 83 · see citing works

Radiology Workflow Disruptors: A Detailed Analysis

Schemmel, A. J., Lee, M., Hanley, T., Pooler, B. D., Kennedy, T. A., Field, A. S., Wiegmann, D. A., & Yu, J. J. (2016) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2016.04.009 · Cited by 82 · see citing works

Deep Learning Enables 60% Accelerated Volumetric Brain MRI While Preserving Quantitative Performance: A Prospective, Multicenter, Multireader Trial

Bash, S., Wang, L., Airriess, C., Zaharchuk, G., Gong, E., Shankaranarayanan, A., & Tanenbaum, L. N. (2021) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7358 · Cited by 80 · see citing works

Radiology reporting—from Hemingway to HAL?

Brady, A. P. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0596-3 · Cited by 77 · see citing works

Improving the Speed of MRI with Artificial Intelligence

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Reconstruction of multicontrast MR images through deep learning

Do, W., Seo, S., Han, Y., Ye, J. C., Choi, S. H., & Park, S. (2019) · Medical Physics · doi:10.1002/mp.14006 · Cited by 75 · see citing works

A Robust Deep Learning Segmentation Method for Hematoma Volumetric Detection in Intracerebral Hemorrhage

Yu, N., Yu, H., Li, H., Ma, N., Hu, C., & Wang, J. (2021) · Stroke · doi:10.1161/strokeaha.120.032243 · Cited by 73 · see citing works

Accuracy of automated patient positioning in CT using a 3D camera for body contour detection

Booij, R., Budde, R. P. J., Dijkshoorn, M. L., & van Straten, M. (2018) · European Radiology · doi:10.1007/s00330-018-5745-z · Cited by 71 · see citing works

Augmented Radiologist Workflow Improves Report Value and Saves Time: A Potential Model for Implementation of Artificial Intelligence

Huy, M., Spear, L. G., Nikpanah, M., Mirmomen, S. M., Machado, L. B., Toscano, A. P., Türkbey, B., Bagheri, M. H., Gulley, J. L., & Folio, L. R. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.09.014 · Cited by 71 · see citing works

Impact of Provider-led, Technology-enabled Radiology Management Program on Imaging

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Strategies for Managing Imaging Utilization

Bernardy, M. O., Ullrich, C. G., Rawson, J. V., Allen, B., Thrall, J. H., Keysor, K. J., James, C., Boyes, J. A., Saunders, W. M., Lomers, W., et al. (2009) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2009.08.003 · Cited by 68 · see citing works

A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients

Gang, Y., Chen, X., Li, H., Wang, H., Li, J., Guo, Y., Zeng, J., Hu, Q., Hu, J., & Xu, H. (2021) · European Radiology · doi:10.1007/s00330-020-07629-4 · Cited by 66 · 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

Motion correction of respiratory-gated PET images using deep learning based image registration framework

Li, T., Zhang, M., Qi, W., Asma, E., & Qi, J. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab8688 · Cited by 63 · see citing works

Machine learning and acute stroke imaging

Sheth, S. A., Giancardo, L., Colasurdo, M., Srinivasan, V. M., Niktabe, A., & Kan, P. T. M. (2022) · Journal of NeuroInterventional Surgery · doi:10.1136/neurintsurg-2021-018142 · Cited by 62 · see citing works

Evaluation of MRI Denoising Methods Using Unsupervised Learning

López, M. M., Frederick, J. M., & Ventura, J. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.642731 · Cited by 58 · see citing works

A convolutional neural network for ultra‐low‐dose CT denoising and emphysema screening

Zhao, T., McNitt‐Gray, M. F., & Ruan, D. (2019) · Medical Physics · doi:10.1002/mp.13666 · Cited by 57 · see citing works

Malpractice in Radiology: What Should You Worry About?

Cannavale, A., Santoni, M., Mancarella, P., Passariello, R., & Arbarello, P. (2013) · Radiology Research and Practice · doi:10.1155/2013/219259 · Cited by 55 · see citing works

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 · see citing works

Reducing inappropriate diagnostic practice through education and decision support

Bairstow, P. J., Persaud, J., Mendelson, R., & Nguyen, L. (2010) · International Journal for Quality in Health Care · doi:10.1093/intqhc/mzq016 · Cited by 55 · see citing works

Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice

Tadavarthi, Y., Makeeva, V., Wagstaff, W., Zhan, H. T., Podlasek, A., Bhatia, N. K., Heilbrun, M. E., Krupinski, E. A., Safdar, N. M., Banerjee, I., et al. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210114 · Cited by 50 · see citing works

Vertical off‐centering affects organ dose in chest CT : Evidence from Monte Carlo simulations in anthropomorphic phantoms

Saltybaeva, N., & Alkadhi, H. (2017) · Medical Physics · doi:10.1002/mp.12519 · Cited by 49 · see citing works

Environmental Factors Predictive of No-Show Visits in Radiology: Observations of Three Million Outpatient Imaging Visits Over 16 Years

Mieloszyk, R. J., Rosenbaum, J. I., Hall, C. S., Hippe, D. S., Gunn, M. L. D., & Bhargava, P. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2018.12.046 · Cited by 49 · see citing works

Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets

Gauriau, R., Bridge, C. P., Chen, L., Kitamura, F., Tenenholtz, N., KIRSCH, J. E., Andriole, K. P., Michalski, M. H., & Bizzo, B. C. (2020) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00308-x · Cited by 47 · see citing works

Computational and human observer image quality evaluation of low dose, knowledge‐based CT iterative reconstruction

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Implementation of Machine Learning Software on the Radiology Worklist Decreases Scan View Delay for the Detection of Intracranial Hemorrhage on CT

Ginat, D. T. (2021) · Brain Sciences · doi:10.3390/brainsci11070832 · Cited by 46 · see citing works

Diverse Applications of Artificial Intelligence in Neuroradiology

Duong, M. T., Rauschecker, A. M., & Mohan, S. (2020) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2020.07.003 · Cited by 45 · see citing works

Automated radiology report summarization using an open-source natural language processing pipeline

Goff, D. J., & Loehfelm, T. W. (2018) · Journal of Digital Imaging · doi:10.1007/s10278-017-0030-2 · Cited by 43 · see citing works

Neural transfer learning for assigning diagnosis codes to EMRs

Rios, A., & Kavuluru, R. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.04.002 · Cited by 43 · see citing works

School of Block–Review of Blockchain for the Radiologists

Abdullah, S. B., Rothenberg, S., Siegel, E. L., & Kim, W. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.06.025 · 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

Deep learning‐based motion quantification from k‐space for fast model‐based magnetic resonance imaging motion correction

Hoßbach, J., Splitthoff, D. N., Cauley, S., Clifford, B., Polak, D., Lo, W., Meyer, H., & Maier, A. (2022) · Medical Physics · doi:10.1002/mp.16119 · Cited by 40 · see citing works

Machine Learning and Coronary Artery Calcium Scoring

Lee, H., Martin, S. S., Burt, J. R., Sahbaee, P., Rapaka, S., Gray, H. N., Leonard, T. J., Schwemmer, C., & Schoepf, U. J. (2020) · Current Cardiology Reports · doi:10.1007/s11886-020-01337-7 · Cited by 37 · see citing works

Generalization Evaluation of Machine Learning Numerical Observers for Image Quality Assessment

Kalayeh, M. M., Marin, T., & Brankov, J. G. (2013) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.2013.2257183 · Cited by 36 · see citing works

Machine Learning and Deep Neural Networks

Eberhard, M., & Alkadhi, H. (2020) · Journal of Thoracic Imaging · doi:10.1097/rti.0000000000000482 · Cited by 36 · see citing works

Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement

Wang, T., Song, N., Liu, L., Zhu, Z., Chen, B., Yang, W., & Chen, Z. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00657-6 · Cited by 34 · see citing works

Automatic Delineation of Gross Tumor Volume Based on Magnetic Resonance Imaging by Performing a Novel Semisupervised Learning Framework in Nasopharyngeal Carcinoma

Liao, W., He, J., Luo, X., Wu, M., Shen, Y., Li, C., Xiao, J., Wang, G., & Chen, N. (2022) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2022.03.031 · Cited by 33 · see citing works

Accelerated Acquisition of High-resolution Diffusion-weighted Imaging of the Brain with a Multi-shot Echo-planar Sequence: Deep-learning-based Denoising

Kawamura, M., Tamada, D., Funayama, S., Kromrey, M., Ichikawa, S., Onishi, H., & Motosugi, U. (2020) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.tn.2019-0081 · Cited by 32 · see citing works

Segmentation of Chronic Subdural Hematomas Using 3D Convolutional Neural Networks

Kellogg, R. T., Vargas, J., Barros, G., Sen, R., Bass, D. I., Mason, J. R., & Levitt, M. R. (2020) · World Neurosurgery · doi:10.1016/j.wneu.2020.12.014 · Cited by 32 · see citing works

Successful Implementation of an Artificial Intelligence-Based Computer-Aided Detection System for Chest Radiography in Daily Clinical Practice

Lee, S., Shin, H. J., Kim, S., & Kim, E.-K. (2022) · Korean Journal of Radiology · doi:10.3348/kjr.2022.0193 · Cited by 31 · see citing works

Decreased Hospital Length of Stay for ICH and PE after Adoption of an Artificial Intelligence-Augmented Radiological Worklist Triage System

Petry, M. J., Lansky, C. A., Chodakiewitz, Y. G., Maya, M. M., & Pressman, B. D. (2022) · Radiology Research and Practice · doi:10.1155/2022/2141839 · Cited by 31 · see citing works

A comparative study of auto-contouring softwares in delineation of organs at risk in lung cancer and rectal cancer

Chen, W., Wang, C., Zhan, W., Jia, Y., Ruan, F., Qiu, L., Yang, S., & Li, Y. (2021) · Scientific Reports · doi:10.1038/s41598-021-02330-y · Cited by 30 · see citing works

Use of Imaging in the Emergency Department: Do Individual Physicians Contribute to Variation?

Valtchinov, V. I., Ip, I. K., Khorasani, R., Schuur, J. D., Zurakowski, D., Lee, J., & Raja, A. S. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.21065 · Cited by 29 · see citing works

Automated Detection of Radiology Reports that Document Non-routine Communication of Critical or Significant Results

Lakhani, P. C., & Langlotz, C. P. (2009) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-009-9237-1 · Cited by 27 · see citing works

Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans

Gibson, E., Georgescu, B., Ceccaldi, P., Trigan, P.-H., Yoo, Y., Das, J., Re, T. J., Vishwanath, R. S., Balachandran, A., Eibenberger, E., et al. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210115 · Cited by 27 · see citing works

Synthetic generation of DSC‐MRI‐derived relative CBV maps from DCE MRI of brain tumors

Sanders, J. W., Chen, H., Johnson, J. M., Schomer, D. F., Jimenez, J. E., Ma, J., & Liu, H. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28432 · Cited by 24 · see citing works

Applying Deep Learning to Accelerated Clinical Brain Magnetic Resonance Imaging for Multiple Sclerosis

Mani, A., Santini, T., Puppala, R., Dahl, M., Venkatesh, S., Walker, E., DeHaven, M., Isitan, C., Ibrahim, T. S., Wang, L., et al. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.685276 · Cited by 24 · see citing works

Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis

Singh, N., Iglesias, J. E., Elfar Adalsteinsson, A., Dalca, A. V., & Golland, P. (2022) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2022-16cc · Cited by 24 · see citing works

Customized Impression Prediction From Radiology Reports Using BERT and LSTMs

Gündoğdu, B., Pamuksuz, U., Chung, J. H., Telleria, J. M., Liu, P., Khan, F., & Chang, P. J. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3086435 · Cited by 22 · see citing works

Assisting radiologists with reporting urgent findings to referring physicians: A machine learning approach to identify cases for prompt communication

Meng, X., Ganoe, C. H., Sieberg, R. T., Cheung, Y. Y., & Hassanpour, S. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103169 · Cited by 21 · see citing works

Clinical utility of deep learning motion correction for T1 weighted MPRAGE MR images

Pawar, K., Chen, Z., Seah, J. C. Y., Law, M., Close, T., & Egan, G. F. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109384 · Cited by 21 · see citing works

Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 T

Herrmann, J., Wessling, D., Nickel, D. M., Arberet, S., Almansour, H., Afat, C., Afat, S., Gassenmaier, S., & Othman, A. E. (2022) · Academic Radiology · doi:10.1016/j.acra.2022.03.018 · Cited by 21 · see citing works

Artificial Intelligence in Quality Improvement: Reviewing Uses of Artificial Intelligence in Noninterpretative Processes from Clinical Decision Support to Education and Feedback

Bhatia, N. K., Trivedi, H., Safdar, N. M., & Heilbrun, M. E. (2020) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.08.002 · Cited by 19 · see citing works

An artificial intelligence‐accelerated 2‐minute multi‐shot echo planar imaging protocol for comprehensive high‐quality clinical brain imaging

Clifford, B., Conklin, J., Huang, S. Y., Feiweier, T., Hosseini, Z. S., Filho, A. L. M. G., Tabari, A., Demir, S., Lo, W., Longo, M. G. F., et al. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29117 · Cited by 19 · see citing works

DICOM Imaging Router: An Open Deep Learning Framework for Classification of Body Parts from DICOM X-ray ScansPreprint

Pham, H., Do, D. V., & Nguyen, H. Q. (2021) · medRxiv · doi:10.1101/2021.08.13.21261945 · Cited by 17 · see citing works

New CMS Clinical Decision Support Regulations: A Potential Opportunity with Major Challenges

Hentel, K., Menard, A., & Khorasani, R. (2017) · Radiology · doi:10.1148/radiol.2017161560 · Cited by 15 · see citing works

Correlate: A PACS- and EHR-integrated Tool Leveraging Natural Language Processing to Provide Automated Clinical Follow-up

Kovacs, M. D., Mesterhazy, J., Avrin, D. E., Urbania, T. H., & Mongan, J. (2017) · Radiographics · doi:10.1148/rg.2017160195 · Cited by 14 · see citing works

Quantification of hematoma and perihematomal edema volumes in intracerebral hemorrhage study: Design considerations in an artificial intelligence validation (QUANTUM) study

Ironside, N. T., Patrie, J. T., Ng, S., Ding, D., Rizvi, T., Kumar, J. S., Mastorakos, P., Hussein, M. Z., Naamani, K. E., Abbas, R., et al. (2022) · Clinical Trials · doi:10.1177/17407745221105886 · Cited by 14 · see citing works

Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application

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Acquisition, preprocessing, and reconstruction of ultralow dose volumetric CT scout for organ‐based CT scan planning

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Should Artificial Intelligence Tell Radiologists Which Study to Read Next?

O’Connor, S. D., & Bhalla, M. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021210009 · Cited by 11 · see citing works

A Proof-of-Concept Study of Artificial Intelligence–assisted Contour Editing

Bai, T., Balagopal, A., Dohopolski, M. J., Morgan, H. E., McBeth, R., Tan, J., Lin, M., Sher, D. J., Nguyen, D. T. D., & Jiang, S. B. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210214 · Cited by 11 · see citing works

From Data to Value: How Artificial Intelligence Augments the Radiology Business to Create Value

Martin-Carreras, T. T., & Chen, P. (2020) · Seminars in Musculoskeletal Radiology · doi:10.1055/s-0039-3400269 · Cited by 10 · see citing works

Federated Deep Learning to More Reliably Detect Body Part for Hanging Protocols, Relevant Priors, and Workflow Optimization

Filice, R. W., Stein, A., Pan, I., & Shih, G. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00547-x · Cited by 10 · see citing works

Evaluation of Traumatic Subdural Hematoma Volume by Using Image Segmentation Assessment Based on Deep Learning

Chen, D., Bian, L., He, H.-Y., Li, Y.-D., Ma, C., & Mao, L.-G. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/3830245 · Cited by 9 · see citing works

Neural Network Detection of Pacemakers for MRI Safety

Thurston, M., Kim, D. H., & Wit, H. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00663-2 · Cited by 8 · see citing works

Quantitative Measurement of Spinal Cerebrospinal Fluid by Cascade Artificial Intelligence Models in Patients with Spontaneous Intracranial Hypotension

Fu, J., Chai, J., Chen, P., Ding, Y.-W., & Chen, H.-C. (2022) · Biomedicines · doi:10.3390/biomedicines10082049 · Cited by 8 · see citing works

“Can We All Get Along?” Cooperative Strategies to Reduce Imaging Overuse

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Clinical language search algorithm from free-text: facilitating appropriate imaging

Chaudhari, G., Chillakuru, Y. R., Chen, T. L., Pedoia, V., Vu, T.-K. H., Hess, C. P., Seo, Y., & Sohn, J. H. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00740-6 · Cited by 5 · see citing works

Contrast allergies for neurological imaging: When to proceed

Fusco, A. F., Pucci, L. A., Pierre, K., Wolberg, A., Small, C. N., Cerillo, J., Siyanaki, M. R. H., & Lucke-Wold, B. P. (2022) · AIMS Allergy and Immunology · doi:10.3934/allergy.2022016 · Cited by 3 · see citing works

Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

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Environmental Sustainability and AI in Radiology: A Double-Edged Sword

Doo, F. X., Vosshenrich, J., Cook, T. S., Moy, L. P., Almeida, E. P. R. P., Woolen, S. A., Gichoya, J. W., Heye, T. J., & Hanneman, K. A. (2024) · Radiology · doi:10.1148/radiol.232030 · Cited by 124 · see citing works

Revolutionizing drug discovery: The impact of artificial intelligence on advancements in pharmacology and the pharmaceutical industry

Yadav, S., Singh, A., Singhal, R., & Yadav, J. P. (2024) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.02.009 · Cited by 108 · see citing works

Evaluation metrics in medical imaging AI: fundamentals, pitfalls, misapplications, and recommendations

Koçak, B., Klontzas, M. E., Stanzione, A., Meddeb, A., Demircioğlu, A., Blüthgen, C., Bressem, K. K., Ugga, L., Mercaldo, N. D., Díaz, O., et al. (2025) · European Journal of Radiology Artificial Intelligence · doi:10.1016/j.ejrai.2025.100030 · Cited by 89 · see citing works

Leading with AI in critical care nursing: challenges, opportunities, and the human factor

Hassan, E. A., & El-Ashry, A. M. (2024) · BMC Nursing · doi:10.1186/s12912-024-02363-4 · Cited by 72 · see citing works

A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports

Jorg, T., Halfmann, M. C., Stoehr, F., Arnhold, G., Theobald, A., Mildenberger, P., & Müller, L. (2024) · Insights into Imaging · doi:10.1186/s13244-024-01660-5 · Cited by 66 · see citing works

Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions

Chen, Y., Lehmann, C. U., & Malin, B. (2024) · Journal of Medical Internet Research · doi:10.2196/60258 · Cited by 75 · see citing works

AI-Driven Advances in Low-Dose Imaging and Enhancement—A Review

David-Olawade, A. C., Olawade, D. B., Vanderbloemen, L., Rotifa, O. B., Fidelis, S. C., Egbon, E., Akpan, A. O., Adeleke, S. M., Ghose, A., & Boussios, S. (2025) · Diagnostics · doi:10.3390/diagnostics15060689 · Cited by 56 · see citing works

Radiology AI and sustainability paradox: environmental, economic, and social dimensions

Koçak, B., Ponsiglione, A., Romeo, V., Ugga, L., Huisman, M., & Cuocolo, R. (2025) · Insights into Imaging · doi:10.1186/s13244-025-01962-2 · Cited by 58 · see citing works

Impact of using an AI scribe on clinical documentation and clinician-patient interactions in allied health private practice: perspectives of clinicians and patients

Evans, K. A., Papinniemi, A., Ploderer, B., Nicholson, V., Hindhaugh, T., Vuvan, V., Cowley, N., Tariq, A., & Thomson, H. (2025) · Musculoskeletal Science and Practice · doi:10.1016/j.msksp.2025.103333 · Cited by 40 · see citing works

The Impact of Artificial Intelligence (AI) on the Accounting System of Saudi Companies

Hamza, R. A. E. M., Alnor, N. H. A., Al‐Matari, E. M., Benzerrouk, Z. S., Mohamed, A. M. E., Bennaceur, M. Y., Elhefni, A. H. M., & Elshaabany, M. M. (2024) · WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS · doi:10.37394/23207.2024.21.42 · Cited by 30 · see citing works

Medical errors and patient safety: Strategies for reducing errors using artificial intelligence

Baurasien, B. K., Alareefi, ‏. S., Almutairi, ‏. B., Alanazi, ‏. M., Alhasson, ‏. H., Alshahrani, A. D., & Almansour, S. A. (2023) · International Journal of Health Sciences · doi:10.53730/ijhs.v7ns1.15143 · Cited by 25 · see citing works

Revolutionizing hematological disorder diagnosis: unraveling the role of artificial intelligence

Obeagu, E. I. (2025) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000003227 · Cited by 17 · see citing works

Agentic systems in radiology: Principles, opportunities, privacy risks, regulation, and sustainability concerns.

Tzanis, E., Adams, L. C., Akinci D’Antonoli, T., Bressem, K. K., Cuocolo, R., Kocak, B., Malamateniou, C., & Klontzas, M. E. (2026) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2025.10.002 · Cited by 16 · see citing works

Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges

Pierre, K., Gupta, M., Raviprasad, A. G., Razavi, S. M. S., Patel, A. B., Peters, K., a, B. H., Mancuso, A., & Forghani, R. (2023) · Expert Review of Anticancer Therapy · doi:10.1080/14737140.2023.2286001 · Cited by 18 · see citing works

Machine Learning in Neuroimaging of Traumatic Brain Injury: Current Landscape, Research Gaps, and Future Directions

Pierre, K., Turetsky, J., Raviprasad, A. G., Razavi, S. M. S., Mathelier, M., Patel, A. B., & Lucke-Wold, B. P. (2024) · Trauma Care · doi:10.3390/traumacare4010004 · Cited by 15 · see citing works

Radiology Workflow Assistance With Artificial Intelligence: Establishing the Link to Outcomes

Gu, Z., Dogra, S., Siriruchatanon, M., Kneifati-Hayek, J. Z., & Kang, S. K. (2025) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2025.10.018 · Cited by 14 · see citing works

Using generative artificial intelligence in clinical practice: a narrative review and proposed agenda for implementation

Scott, I. A., Reddy, S., Kelly, T., Miller, T., & van der Vegt, A. H. (2025) · The Medical Journal of Australia · doi:10.5694/mja2.70057 · Cited by 15 · see citing works

Factors influencing AI acceptance in radiology: a systematic review across the radiology workflow

Verwey, J., de Zwart, B. H. C., IJzerman, M. J., Visser, J. J., & Sülz, S. (2025) · European Radiology · doi:10.1007/s00330-025-12129-4 · Cited by 16 · see citing works

Adoption of artificial intelligence applications in clinical practice: Insights from a Survey of Healthcare Organizations in Lombardy, Italy

Ardito, V., Cappellaro, G., Compagni, A., Petracca, F., & Preti, L. M. (2025) · Digital Health · doi:10.1177/20552076251355680 · Cited by 14 · see citing works

The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection

Li, L., Peng, M., Zou, Y., Li, Y., & Qiao, P. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1514931 · Cited by 12 · 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

Towards an Integrated Framework for Health Surveillance Systems: A Systematic Literature Review of Design Components and Implementation Challenges

Mollabagher, M., Hassanzadeh, A., Sepehri, M. M., Habibelahi, A., & Sarabadani, A. (2026) · Health Science Reports · doi:10.1002/hsr2.71652 · Cited by 13 · see citing works

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

Vasile, C. M., & Iriart, X. (2023) · Diagnostics · doi:10.3390/diagnostics13193137 · Cited by 10 · see citing works

Selecting the Best Radiology Workflow Efficiency Applications

Bharadwaj, P., Berger, M., Blumer, S. L., & Lobig, F. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01146-2 · Cited by 9 · see citing works

The role of patient outcomes in shaping moral responsibility in AI-supported decision making

Edwards, C. R., Murphy, A., Singh, A., Daniel, S., & Chamunyonga, C. (2025) · Radiography · doi:10.1016/j.radi.2025.102948 · Cited by 9 · see citing works

La regulación legal de la inteligencia artificial en la Unión Europea: guía práctica para radiólogos

Santos, Á. M., Lojo‐Lendoiro, S., Cañellas, M. R., & Solís, P. V. (2024) · Radiología · doi:10.1016/j.rx.2023.11.008 · Cited by 8 · see citing works

Physician-in-the-Loop Active Learning in Radiology Artificial Intelligence Workflows: Opportunities, Challenges, and Future Directions

Luo, M., Yousefirizi, F., Rouzrokh, P., Jin, W., Alberts, I., Gowdy, C., Bouchareb, Y., Hamarneh, G., Klyuzhin, I. S., & Rahmim, A. (2025) · American Journal of Roentgenology · doi:10.2214/ajr.25.33364 · Cited by 7 · see citing works

Behavioral Impacts of AI Reliance in Diagnostics: Balancing Automation with Skill Retention

Sunday, O. (2025) · Epidemiology and Health Data Insights · doi:10.63946/ehdi/16894 · Cited by 6 · see citing works

Applications of artificial intelligence in thoracic imaging: a review

Kalyanpur, A., & Mathur, N. (2025) · Academia Medicine · doi:10.20935/acadmed7509 · Cited by 6 · see citing works

State of the AI: Post-Deployment Monitoring of Radiology-Focused Internally Developed AI

Cook, C. J., Klug, J. R., Kandler, B. W., Suárez, A. B., Dachowicz, A. P., Blezek, D. J., Missert, A. D., Conte, G. M., Benfield, J. D., Mensing-Diggs, A., et al. (2026) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2026.100342 · Cited by 5 · see citing works

Abdominal and Pelvic MRI Protocol Prediction Using Natural Language Processing

Warner, J., Hartman, R. P., Blezek, D. J., & Thomas, J. V. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01395-9 · Cited by 5 · see citing works

A Machine Learning-Based Web Application for Heart Disease Prediction

Gabriel, J. (2024) · Intelligent Control and Automation · doi:10.4236/ica.2024.151002 · Cited by 5 · see citing works

The legal regulation of artificial intelligence in the European Union: A practical guide for radiologists

Santos, Á. M., Lendoiro, S. L., Cañellas, M. R., & Solís, P. V. (2024) · Radiología (English Edition) · doi:10.1016/j.rxeng.2023.11.011 · Cited by 6 · see citing works

Artificial intelligence in radiology

Elizondo‐Riojas, G., Negreros-Osuna, A. A., Bernal-Ramirez, J. M., & Conde-Castro, B. (2024) · Journal of the Mexican Federation of Radiology and Imaging · doi:10.24875/jmexfri.m24000073 · Cited by 6 · 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

Emerging Value-Based Radiology in the Era of Artificial Intelligence

Mun, S. K., Lo, S. B., Wong, K. H., Koh, D., & Prior, F. (2023) · Medical Research Archives · doi:10.18103/mra.v11i5.3915 · Cited by 4 · see citing works

More than a comment: Radiographers' perspectives on image interpretation and image quality

Tonks, A., Hewis, J., Jimenez, Y., Gray, F., & Ekpo, E. U. (2026) · Radiography · doi:10.1016/j.radi.2026.103343 · Cited by 3 · see citing works

The role of artificial intelligence in the interpretation and review of images across different radiology modalities with a focus on its benefits and challenges

Hooshmandi, S., Tabatabaii, M., Arani, H. Z., Heidarian, A., & Nazemi, S. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-00843-x · Cited by 3 · see citing works

A modern radiologist’s guide to artificial intelligence

Kapur, J., Kelly, B. S., & Vega, R. (2026) · Pediatric Radiology · doi:10.1007/s00247-026-06542-0 · Cited by 3 · see citing works

Perceptions of artificial intelligence among computed tomography technologists in Saudi Arabia: Influence of demographics and training on AI adoption

Alghamdi, S. A., Alashban, Y. I., Alhailiy, A. B., Alharbi, F. F., & Al-Nahrawi, A. E. (2025) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2025.101355 · Cited by 3 · see citing works

X-ray modalities in the era of artificial intelligence: overview of self-supervised learning approach

Martinović, I., Mao, S., Dousty, M., Li, W., Djukanović, M., Colak, E., & Sejdić, E. (2025) · FACETS · doi:10.1139/facets-2024-0229 · Cited by 3 · see citing works

Digital innovations for sustainable radiology: Reducing waste, emissions, and inefficiencies in imaging workflows

Toker, I., Jansen, S., Knoche, J., & Lorenz, D. (2025) · Digital Health · doi:10.1177/20552076251386788 · Cited by 3 · see citing works

Artificial Intelligence in Computed Tomography Radiology: A Systematic Review on Risk Reduction Potential

Coelho, S., Fernandes, A., Freitas, M. A. C., & Fernandes, R. J. (2025) · Applied Sciences · doi:10.3390/app15179659 · Cited by 2 · see citing works

Quantitative evaluation of an artificial intelligence-assisted platform in CT acquisition workflow

Caballo, M., McLennan, L., Benbow, M., Condron, M., Foden, A., Thomas, S., & Bull, R. K. (2025) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2025.102133 · Cited by 2 · see citing works

Impact of a Digital Intelligence Platform on Radiology Workflow Efficiency and Patient Satisfaction: A Retrospective Cohort Study

Feng, J., Cao, X., Fang, J., Cai, L., Wang, Q., & Zhang, J. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.03.017 · Cited by 2 · see citing works

Effective hybrid video denoising and blending framework for Internet of Remote Things (IoRT) environments

Devi, B. A., & Choudhry, M. D. (2024) · Automatika · doi:10.1080/00051144.2024.2312747 · Cited by 2 · see citing works

Künstliche Intelligenz im Dosismanagement der diagnostischen Radiologie

Garajová, L., Garbe, S., & Sprinkart, A. M. (2024) · Die Radiologie · doi:10.1007/s00117-024-01330-z · Cited by 2 · see citing works

Independent Evaluation of a Commercial AI Software for Incidental Findings of Pulmonary Embolism (IPE) on a Large Hospital Retrospective Dataset

Ambrogio, S., Verdon, I., Laureano, B., Ramnarine, K. V., Fedele, F., Vilić, D., Honey, I. D., Barton, E., Gonçalves, C. P. Q., Mak, S. M., et al. (2025) · Radiology Research and Practice · doi:10.1155/rrp/9091895 · Cited by 2 · see citing works

The paradox of AI assistance: enhancing quality while hindering efficiency in local hospitals

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Deep Learning Approach for Pneumonia Prediction from X-Rays using A Pretrained Densenet Model

Wafi, A. Z. A., Rochim, F. P., & Fathimah, A. (2025) · Jurnal ELTIKOM · doi:10.31961/eltikom.v9i1.1457 · Cited by 2 · see citing works

AI-Driven Integration and Workflow Optimization in Modern Healthcare Facilities

Usharani, S., Manju Bala, P., Devi, A., Rajkumar, K., & Saravanan, D. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2787-7.ch009 · Cited by 2 · see citing works

Artificial Intelligence Applications in Medical Imaging

Seeram, E., & Kanade, V. A. (2024) · Springer eBooks · doi:10.1007/978-3-031-64049-0_6 · Cited by 1 · see citing works

Workflow Integration and Training

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Enhancing Zero-shot Learning in Chest X-ray Diagnostics using BioBERT for Textual Representation

Bhardwaj, P., Bhat, S., & Maier, A. (2025) · Informatik aktuell · doi:10.1007/978-3-658-47422-5_61 · Cited by 1 · see citing works

AI-based Diagnostic X-Ray Quality Assurance

Mbire, P., Chitiza, K. T., Dzingirai, K. P., Sadock, B., & Dube, T. L. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9301-7.ch002 · Cited by 1 · see citing works

Artificial Intelligence and Machine Learning Approaches in Radiology for Medical Imaging Diagnostics

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X-ray imaging techniques for damage detection and characterisation

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Radiology Staff Experiences With Integrating Artificial Intelligence Into Radiology Practice in a Swedish Hospital: Qualitative Case Study

Nilsén, P., Svedberg, P., Larsson, I., Petersson, L., Nygren, J., Steerling, E., & Neher, M. (2025) · JMIR Formative Research · doi:10.2196/77843 · Cited by 3 · see citing works

The role of artificial intelligence in optimizing radiation dose in diagnostic imaging

Dehbaghi, H. A., & Khoshgard, K. (2026) · Health and Technology · doi:10.1007/s12553-025-01049-4 · Cited by 1 · see citing works

Artificial intelligence and breast cancer screening in Serbia: a dual-perspective qualitative study among radiologists and screening-aged women

Jovanović, S., Vukičević, J. P., Kilibarda, B., Milosavljević, M., & Bjegovic-Mikanovic, V. M. (2026) · Frontiers in Radiology · doi:10.3389/fradi.2026.1714029 · Cited by 1 · see citing works

Evaluation of large language models with clinical guidance for vetting outpatient magnetic resonance imaging lumbar spine referrals

Clackett, W., Alsusa, H., Watson, H., Kascenas, A., Scott, D., Kanodia, A. K., Barry, O. T., & O’Neil, A. Q. (2026) · Scottish Medical Journal · doi:10.1177/00369330261441582 · Cited by 1 · see citing works

AI-Driven Operational Improvements in Healthcare

Eswaran, U., Eswaran, V., Eswaran, V., & Murali, K. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1022-0.ch008 · Cited by 1 · see citing works

Enhancing Healthcare with AI: Diagnostic Imaging and Patient Sentiment Analysis

Abimbola, B. (2024) · American Journal of Biomedical Science & Research · doi:10.34297/ajbsr.2024.21.002912

Real-World Adoption of Artificial Intelligence in Radiology: Opportunities and Barriers

Forghani, R. (2024) · American Journal of Neuroradiology · doi:10.3174/ajnr.a8422

Artificial Intelligence in Medical Imaging Technology at a Glance

Seeram, E., & Kanade, V. A. (2024) · Springer eBooks · doi:10.1007/978-3-031-64049-0_1 · Cited by 1 · see citing works

Integrating Artificial Intelligence into Radiology Practice: A Qualitative Interview Study of Swedish Radiology Staff Experiences (Preprint)Preprint

Nilsén, P., Svedberg, P., Larsson, I., Petersson, L., Nygren, J., Steerling, E., & Neher, M. (2025) · doi:10.2196/preprints.77843

Implementation Science: Definition, Importance, and Application in Neuroradiology

Rashidi, A., & Sadigh, G. (2025) · American Journal of Neuroradiology · doi:10.3174/ajnr.a9104

Determinants of AI-enabled customer experience across healthcare sectors: a decade in review

Pramodh, K., Nayak, S., Chavadi, C. A., & Pai, Y. P. (2025) · Cogent Business & Management · doi:10.1080/23311975.2025.2590241

A clinically validated AI framework for kidney cancer detection and characterization

Petryshak, B., Iljin, M., Denissova, A., Ariva, J., Häide, T., Lasn, V., Reim, M., Ivaniv, I., Lillsaar, T., Vilt, H., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-01264-0

Leveraging Artificial Intelligence and Internet of Things for Advancements in Medical Imaging: Applications, Challenges, and Future Directions

Mousania, Y., & Karimi, S. (2025) · doi:10.1109/ipria68579.2025.11263729

Evaluating the Effectiveness of Online Digital Platforms in Streamlining Registration Processes with the Securities and Exchange Commission in Bondoc Peninsula

Requiz, M., & Espiritu, M. (2026) · Psychology and Education A Multidisciplinary Journal · doi:10.70838/pemj.510608

Improving non-expert performance in musculoskeletal MRI protocoling through a large language model

Lee, S., Choi, H., Chun, K. S., Lee, S., Park, H., Le, T. D., Mukherjee, P., Lee, J., Chang, S., Lee, S.-Y., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-41898-1

Measuring AI preparedness in health professions education: Evidence from a national survey of medical radiation science students and new graduates

Edwards, C. R., Murphy, A., Gunn, T. A., Singh, A., Arruzza, E. S., Makanjee, C. R., Geritz, E., & Chamunyonga, C. (2026) · Radiography · doi:10.1016/j.radi.2026.103438

The future of magnetic resonance imaging: our experience and literature review of actual tendencies

Chervenkov, L., Doykov, M., Velikova, T. V., Kostov, G., & Hristov, B. K. (2026) · Pharmacia · doi:10.3897/pharmacia.73.e164978

Digitally-Augmented Radiology: From tablets to AI, a narrative review of technologies empowering the modern radiologist

Saliba, T., Rotzinger, D. C., & Fahrni, G. (2026) · F1000Research · doi:10.12688/f1000research.182574.1

The Impact of Artificial Intelligence in Medical Imaging: Trends, Gaps, and Future Direction

Parthasarathy, A. R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-25694-2_3

Optimizing AI Onboarding in Radiology: Impact of Training Case Number Using Multiple Sclerosis Lesions on 3D FLAIR, a pilot studyPreprint

Ludovichetti, R., Pawlus, K., Chhugani, A., Chhugani, Y., Nierobisch, N., Federau, C., Kulcsár, Z., & Hainc, N. (2026) · Research Square · doi:10.21203/rs.3.rs-10445073/v1

A Node-Based Workflow Management System for End-to-End and Human-in-the-Loop Medical Image Processing

Yurtsever, M. M. E., Eken, S., & Sagiroglu, S. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02305-3

Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics

Guillon, L. L., Camirand, D., Guilbert, F., & Forghani, R. (2020) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2020.08.008 · Cited by 84 · see citing works

Artificial Intelligence in Quality Improvement: Reviewing Uses of Artificial Intelligence in Noninterpretative Processes from Clinical Decision Support to Education and Feedback

Bhatia, N. K., Trivedi, H., Safdar, N. M., & Heilbrun, M. E. (2020) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.08.002 · Cited by 19 · 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

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

Radiology Workflow Assistance With Artificial Intelligence: Establishing the Link to Outcomes

Gu, Z., Dogra, S., Siriruchatanon, M., Kneifati-Hayek, J. Z., & Kang, S. K. (2025) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2025.10.018 · Cited by 14 · see citing works