Continuous Learning AI in Radiology: Implementation Principles and Early Applications

The FAIR Guiding Principles for scientific data management and stewardship

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Continual lifelong learning with neural networks: A review

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The practical implementation of artificial intelligence technologies in medicine

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Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

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Predicting cancer outcomes from histology and genomics using convolutional networks

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Current Applications and Future Impact of Machine Learning in Radiology

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Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging

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FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare

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The current and future state of AI interpretation of medical images

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Blockchain and Artificial Intelligence Technology in E-Health

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Temporal quality degradation in AI models

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Deep Learning: An Update for Radiologists

Cheng, P. M., Montagnon, E., Yamashita, R., Pan, I., Cadrin-Chênevert, A., Romero, F., Chartrand, G., Kadoury, S., & Tang, A. (2021) · Radiographics · doi:10.1148/rg.2021200210 · Cited by 179

Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification

Eche, T., Schwartz, L. H., Mokrane, F.-Z., & Dercle, L. (2021) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2021210097 · Cited by 163

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning—A Comprehensive Review

Kazimierczak, N., Kazimierczak, W., Serafin, Z., Nowicki, P., Nożewski, J., & Janiszewska‐Olszowska, J. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13020344 · Cited by 170

Understanding and Mitigating Bias in Imaging Artificial Intelligence

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A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences

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Clinical applications of artificial intelligence in radiology

Mello-Thoms, C. R., & Mello, C. A. B. (2023) · British Journal of Radiology · doi:10.1259/bjr.20221031 · Cited by 141

Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers

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Transparent medical image AI via an image–text foundation model grounded in medical literature

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Quantifying Uncertainty in Deep Learning of Radiologic Images

Faghani, S., Moassefi, M., Rouzrokh, P., Khosravi, B., Baffour, F. I., Ringler, M. D., & Erickson, B. J. (2023) · Radiology · doi:10.1148/radiol.222217 · Cited by 125

Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations

Hickman, S. E., Baxter, G. C., & Gilbert, F. J. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01333-w · Cited by 110

Dynamic Memory to Alleviate Catastrophic Forgetting in Continual Learning with Medical Imaging

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Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah

Voets, M. M., Veltman, J., Slump, C. H., Siesling, S., & Koffijberg, H. (2022) · Value in Health · doi:10.1016/j.jval.2021.11.1362 · Cited by 105

Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA

Brady, A. P., Allen, B., Chong, J., Kotter, E., Kottler, N. E., Mongan, J., Oakden‐Rayner, L., dos Santos, D. P., Tang, A., Wald, C., et al. (2024) · Insights into Imaging · doi:10.1186/s13244-023-01541-3 · Cited by 106

Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review

Motwani, A., Shukla, P. K., & Pawar, M. K. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102431 · Cited by 97

Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency

Montero, A. M. B., Bibal, A., Huet‐Dastarac, M., Draguet, C., Valdés, G., Nguyen, D. T. D., Willems, S., Vandewinckele, L., Holmström, M., Löfman, F., et al. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac678a · Cited by 91

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 88

Evaluation and Real-World Performance Monitoring of Artificial Intelligence Models in Clinical Practice: Try It, Buy It, Check It

Allen, B., Dreyer, K. J., Stibolt, R., Agarwal, S., Coombs, L. P., Treml, C., Elkholy, M., Brink, L., & Wald, C. (2021) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2021.08.022 · Cited by 80

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA

Brady, A. P., Allen, B., Chong, J., Kotter, E., Kottler, N. E., Mongan, J., Oakden‐Rayner, L., dos Santos, D. P., Tang, A., Wald, C., et al. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.230513 · Cited by 88

Understanding Biases and Disparities in Radiology AI Datasets: A Review

Tripathi, S., Gabriel, K., Dheer, S., Parajuli, A., Augustin, A. I., Elahi, A., Awan, O., & Dako, F. (2023) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2023.06.015 · Cited by 75

Considerations for environmental sustainability in clinical radiology and radiotherapy practice: A systematic literature review and recommendations for a greener practice

Anudjo, M. N. K., Vitale, C. H., Elshami, W., Hancock, A., Adeleke, S. M., Franklin, J. M., & Akudjedu, T. N. (2023) · Radiography · doi:10.1016/j.radi.2023.09.006 · Cited by 75

AI in radiology: From promise to practice − A guide to effective integration

Katal, S., York, B. R., & Gholamrezanezhad, A. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111798 · Cited by 75

Patients’ Trust in Artificial Intelligence–based Decision-making for Localized Prostate Cancer: Results from a Prospective Trial

Rodler, S., Kopliku, R., Ulrich, D., Kaltenhauser, A., Casuscelli, J., Eismann, L., Waidelich, R., Buchner, A., Butz, A., Cacciamani, G. E., et al. (2024) · European Urology Focus · doi:10.1016/j.euf.2023.10.020 · Cited by 65

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Tripathi, S., Tabari, A., Mansur, A., Dabbara, H., Bridge, C. P., & Daye, D. (2024) · Diagnostics · doi:10.3390/diagnostics14020174 · Cited by 70

Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning

Lemay, A., Gros, C., Zhuo, Z., Zhang, J., Duan, Y., Cohen‐Adad, J., & Liu, Y. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102766 · Cited by 67

How AI challenges the medical device regulation: patient safety, benefits, and intended uses

Onitiu, D., Wachter, S., & Mittelstadt, B. (2024) · Journal of Law and the Biosciences · doi:10.1093/jlb/lsae007 · Cited by 61

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

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Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA

Brady, A. P., Allen, B., Chong, J., Kotter, E., Kottler, N. E., Mongan, J., Oakden‐Rayner, L., dos Santos, D. P., Tang, A., Wald, C., et al. (2024) · Canadian Association of Radiologists Journal · doi:10.1177/08465371231222229 · Cited by 59

Artificial intelligence in radiology: 173 commercially available products and their scientific evidence

Antonissen, N. J. C., Tryfonos, O., Houben, I. B., Jacobs, C., de Rooij, M., & van Leeuwen, K. G. (2025) · European Radiology · doi:10.1007/s00330-025-11830-8 · Cited by 48

Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi‐society statement from the ACR, CAR, ESR, RANZCR & RSNA

Brady, A. P., Allen, B., Chong, J., Kotter, E., Kottler, N. E., Mongan, J., Oakden‐Rayner, L., dos Santos, D. P., Tang, A., Wald, C., et al. (2024) · Journal of Medical Imaging and Radiation Oncology · doi:10.1111/1754-9485.13612 · Cited by 50

Sustainable deployment of clinical prediction tools—a 360° approach to model maintenance

Davis, S. E., Embí, P. J., & Matheny, M. E. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae036 · Cited by 46

Enhancing Postmarketing Surveillance of Medical Products With Large Language Models

Matheny, M. E., Yang, J., Smith, J., Walsh, C. G., Al-Garadi, M. A., Davis, S. E., Marsolo, K. A., Fabbri, D., Reeves, R. R., Johnson, K. B., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.28276 · Cited by 42

The AI revolution in glaucoma: Bridging challenges with opportunities

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Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts

Linguraru, M. G., Bakas, S., Aboian, M., Chang, P., Flanders, A. E., Kalpathy–Cramer, J., Kitamura, F., Lungren, M. P., Mongan, J., Prevedello, L. M. S., et al. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.240225 · Cited by 43

Artificial Intelligence–Based Data Corrections for Attenuation and Scatter in Position Emission Tomography and Single-Photon Emission Computed Tomography

McMillan, A. B., & Bradshaw, T. (2021) · PET Clinics · doi:10.1016/j.cpet.2021.06.010 · Cited by 39

Pitfalls in Interpretive Applications of Artificial Intelligence in Radiology

Behzad, S., Tabatabaei, S. M. H., Lu, M. Y., Eibschutz, L. S., & Gholamrezanezhad, A. (2024) · American Journal of Roentgenology · doi:10.2214/ajr.24.31493 · Cited by 37

Resilience-aware MLOps for AI-based medical diagnostic system

Москаленко, В. В., & Kharchenko, V. S. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1342937 · Cited by 38

The U.S. Radiologist Workforce: AJR Expert Panel Narrative Review

Rozenshtein, A., Findeiss, L., Wood, M. J., Shih, G., & Parikh, J. R. (2024) · American Journal of Roentgenology · doi:10.2214/ajr.24.32085 · Cited by 36

Continual learning in medical image analysis: A comprehensive review of recent advancements and future prospects

Kumari, P., Chauhan, J., Bozorgpour, A., Huang, B., Azad, R., & Merhof, D. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103730 · Cited by 46

The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus

Giansanti, D., & Basilio, F. D. (2022) · Healthcare · doi:10.3390/healthcare10030509 · Cited by 33

Machine Learning Model Drift: Predicting Diagnostic Imaging Follow-Up as a Case Example

Lacson, R. C., Eskian, M., Licaros, A., Kapoor, N., & Khorasani, R. (2022) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2022.05.030 · Cited by 33

Performance of a Breast Cancer Detection AI Algorithm Using the Personal Performance in Mammographic Screening Scheme

Chen, Y., Taib, A. G., Darker, I. T., & James, J. J. (2023) · Radiology · doi:10.1148/radiol.223299 · Cited by 33

Navigating the AI Landscape in Medical Imaging: A Critical Analysis of Technologies, Implementation, and Implications

Sosna, J., Joskowicz, L., & Saban, M. (2025) · Radiology · doi:10.1148/radiol.240982 · Cited by 34

Toward an Automated Learning Control Architecture for Cyber-Physical Manufacturing Systems

Kovalenko, I., Moyne, J. R., Bi, M., Balta, E. C., Ma, W., Qamsane, Y., Zhu, X., Mao, Z. M., Tilbury, D. M., & Barton, K. L. (2022) · IEEE Access · doi:10.1109/access.2022.3165551 · Cited by 31

Impact of generative artificial intelligence models on the performance of citizen data scientists in retail firms

Abumalloh, R. A., Nilashi, M., Ooi, K., Tan, G. W., & Chan, H. K. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104128 · Cited by 31

Lessons learned in transitioning to AI in the medical imaging of COVID-19

Naqa, I. M. E., Hu, Q., Chen, W., Li, H., Fuhrman, J. D., Gorre, N., & Giger, M. L. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.s1.010902 · Cited by 30

What is Wrong with Continual Learning in Medical Image Segmentation?

González, C., Lemke, N., Ranem, A., Sakas, G., & Mukhopadhyay, A. (2025) · doi:10.1145/3746259.3760435 · Cited by 27

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA

Brady, A. P., Allen, B., Chong, J., Kotter, E., Kottler, N. E., Mongan, J., Oakden‐Rayner, L., dos Santos, D. P., Tang, A., Wald, C., et al. (2024) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2023.12.005 · Cited by 29

Artificial Intelligence and Dentomaxillofacial Radiology Education: Innovations and Perspectives

Negrete, D., Lopes, S. L. P. D. C., Barretto, M. D. D. A., de Moura, N. B., Nahás-Scocate, A. C. R., & Costa, A. L. F. (2025) · Dentistry Journal · doi:10.3390/dj13060245 · Cited by 26

Radiology and the Climate Crisis: Opportunities and Challenges—Radiology In Training

Buckley, B. W., & MacMahon, P. J. (2021) · Radiology · doi:10.1148/radiol.2021210851 · Cited by 26

Deployment of Artificial Intelligence in Radiology: Strategies for Success

Jiang, S., Bukhari, S. M. A., Krishnan, A., Bera, K., Sharma, A., Caovan, D., Rosipko, B., & Gupta, A. B. (2024) · American Journal of Roentgenology · doi:10.2214/ajr.24.31898 · Cited by 24

A roadmap to implementing machine learning in healthcare: from concept to practice

Yan, A. P., Guo, L. L., Inoue, J., Arciniegas, S. E., Vettese, E., Wolochacz, A., Crellin‐Parsons, N., Purves, B., Wallace, S., Patel, A., et al. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1462751 · Cited by 24

An EANM position paper on the application of artificial intelligence in nuclear medicine

Hustinx, R., Pruim, J., Laßmann, M., & Visvikis, D. (2022) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-022-05947-x · Cited by 24

Critical Appraisal of Artificial Intelligence–Enabled Imaging Tools Using the Levels of Evidence System

Pham, N., Hill, V. B., Rauschecker, A. M., Lui, Y. W., Niogi, S. N., Filippi, C. G., Chang, P., Zaharchuk, G., & Wintermark, M. (2023) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7850 · Cited by 26

Medical machine learning operations: A framework to facilitate clinical AI development and deployment in radiology

de Almeida, J. G., Messiou, C., Withey, S. J., Matos, C., Koh, D.-M., & Papanikolaou, N. (2025) · European Radiology · doi:10.1007/s00330-025-11654-6 · Cited by 23

MLHOps: Machine Learning Health Operations

Khattak, F. K., Subasri, V., Krishnan, A., Pou-Prom, C., Koçak, S. A., Dolatabadi, E., Pandya, D., Seyyed-Kalantari, L., & Rudzicz, F. (2024) · IEEE Access · doi:10.1109/access.2024.3521279 · Cited by 31

Adversarial Continual Learning for Multi-domain Hippocampal Segmentation

Memmel, M., González, C., & Mukhopadhyay, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87722-4_4 · Cited by 23

Continual Hippocampus Segmentation with Transformers

Ranem, A., González, C., & Mukhopadhyay, A. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00415 · Cited by 22

Reconfiguration of uncertainty: Introducing AI for prediction of mortality at the emergency department

Tyskbo, D., & Nygren, J. (2024) · Social Science & Medicine · doi:10.1016/j.socscimed.2024.117298 · Cited by 24

Updates in Artificial Intelligence for Breast Imaging

Bahl, M. (2021) · Seminars in Roentgenology · doi:10.1053/j.ro.2021.12.005 · Cited by 20

The time is now: making the case for a UK registry of deployment of radiology artificial intelligence applications

Silkens, M., Ross, J., Hall, M., Scarbrough, H., & Rockall, A. (2023) · Clinical Radiology · doi:10.1016/j.crad.2022.09.132 · Cited by 19

CIRSE Position Paper on Artificial Intelligence in Interventional Radiology

Najafi, A., Cazzato, R. L., Meyer, B. C., Pereira, P. L., Alberich, A., López‐Rueda, A., Ronot, M., Fritz, J. M., Maas, M., Benson, S., et al. (2023) · CardioVascular and Interventional Radiology · doi:10.1007/s00270-023-03521-y · Cited by 20

Boosting knowledge diversity, accuracy, and stability via tri-enhanced distillation for domain continual medical image segmentation

Zhu, Z., Ma, X., Wang, W., Dong, S., Wang, K., Wu, L., Luo, G., Wang, G., & Li, S. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103112 · Cited by 20

Reforming China’s Secondary Vocational Medical Education: Adapting to the Challenges and Opportunities of the AI Era

Tong, W., Zhang, X., Zeng, H., Pan, J., Gong, C., & Zhang, H. (2024) · JMIR Medical Education · doi:10.2196/48594 · Cited by 19

The Evolving Role of Artificial Intelligence in Radiotherapy Treatment Planning—A Literature Review

Kalsi, S., French, H., Chhaya, S., Madani, H., Mir, R., Anosova, A., & Dubash, S. (2024) · Clinical Oncology · doi:10.1016/j.clon.2024.06.005 · Cited by 19

Perspectives of medical imaging professionals about the impact of AI on Swiss radiographers

Champendal, M., Labouchère, S. D., Ghotra, S. S., Gremion, I., Sun, Z., Torre, S., Khine, R., Marmy, L., Malamateniou, C., & dos Reis, C. S. (2024) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2024.101741 · Cited by 19

Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization

Fathi, M., Eshraghi, R., Behzad, S., Tavasol, A., Bahrami, A., Tafazolimoghadam, A., Bhatt, V. V., Ghadimi, D. J., & Gholamrezanezhad, A. (2024) · Emergency Radiology · doi:10.1007/s10140-024-02278-2 · Cited by 19

Remote Ultrasound Scan Procedures with Medical Robots: Towards New Perspectives between Medicine and Engineering

Bucolo, M. A. R., Bucolo, G., Buscarino, A., Fiumara, A., Fortuna, L., & Gagliano, S. (2022) · Applied Bionics and Biomechanics · doi:10.1155/2022/1072642 · Cited by 19

Radiology 2040

Brink, J. A., & Hricak, H. (2022) · Radiology · doi:10.1148/radiol.222594 · Cited by 19

Perioperative risk scores: prediction, pitfalls, and progress

Bedford, J., Redfern, O., O’Brien, B., & Watkinson, P. (2024) · Current Opinion in Anaesthesiology · doi:10.1097/aco.0000000000001445 · Cited by 18

Development and External Validation of an Artificial Intelligence Model for Identifying Radiology Reports Containing Recommendations for Additional Imaging

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Developing a Framework for Self-regulatory Governance in Healthcare AI Research: Insights from South Korea

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