Continuous Learning AI in Radiology: Implementation Principles and Early Applications

The FAIR Guiding Principles for scientific data management and stewardship

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Overcoming catastrophic forgetting in neural networks

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Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning

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Curriculum learning

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Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

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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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Using a Large Language Model for Postdeployment Monitoring of FDA-Approved Artificial Intelligence: Pulmonary Embolism Detection Use Case

Sorin, V., Korfiatis, P. D., Bratt, A. K., Leiner, T., Wald, C., Butler, C. S., Cook, C. J., Kline, T. L., & Collins, J. D. (2025) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2025.06.036 · Cited by 9

Diagnostic accuracy differences in detecting wound maceration between humans and artificial intelligence: the role of human expertise revisited

Kücking, F., Hübner, U., & Busch, D. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf116 · Cited by 9

ETHICS of AI Adoption and Deployment in Health Care: Progress, Challenges, and Next Steps

Oleribe, O. O., Taylor-Robinson, A. W., Chimezie, C., & Taylor-Robinson, S. D. (2025) · JMIR AI · doi:10.2196/67626 · Cited by 9

Impact of continuous learning on diagnostic breast MRI AI: evaluation on an independent clinical dataset

Li, H., Whitney, H. M., Ji, Y., Edwards, A. V., Papaioannou, J., Liu, P., & Giger, M. L. (2022) · Journal of medical imaging · doi:10.1117/1.jmi.9.3.034502 · Cited by 9

Diagnostic study on clinical feasibility of an AI-based diagnostic system as a second reader on mobile CT images: a preliminary result

Diao, K., Chen, Y., Liu, Y., Chen, B., Li, W., Zhang, L., Qu, Y.-L., Zhang, T., Zhang, Y., Wu, M. C., et al. (2022) · Annals of Translational Medicine · doi:10.21037/atm-22-2157 · Cited by 9

Exploring the landscape of automatic cerebral microbleed detection: A comprehensive review of algorithms, current trends, and future challenges

Ferlin, M. A., Klawikowska, Z., Grochowski, M., Grzywińska, M., & Szurowska, E. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120655 · Cited by 10

AI in radiology: is it the time for randomized controlled trials?

Gorenstein, L., Soffer, S., Apter, S., Konen, E., & Klang, E. (2023) · European Radiology · doi:10.1007/s00330-022-09381-3 · Cited by 9

Exploring the interplay of clinical reasoning and artificial intelligence in psychiatry: Current insights and future directions

Gauld, C., Martin, V., Bottemanne, H., Fourneret, P., Micoulaud‐Franchi, J., & Dumas, G. (2024) · Psychiatry Research · doi:10.1016/j.psychres.2024.116255 · Cited by 8

Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities

Dreizin, D., Khatri, G. D., Staziaki, P. V., Buch, K. A., Unberath, M., Mohammed, M., Sodickson, A. D., Khurana, B., Agrawal, A., Spann, J. S., et al. (2024) · Emergency Radiology · doi:10.1007/s10140-024-02306-1 · Cited by 8

Artificial Intelligence for Risk Stratification of Acute Pulmonary Embolism: Perspectives on Clinical Needs, Expanding Toolkit, and Pathways Forward

Henkin, S., Houghton, D. E., Hunsaker, A. R., Zuin, M., Pfeferman, M. B., Sato, A., & Piazza, G. (2025) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2025.05.025 · Cited by 9

Detection accuracy of an AI platform for dental treatment features on panoramic radiographs – tooth- and patient-level analyses

Kazimierczak, N., Sultani, N., Chwarścianek, N., Krzykowski, S., Janiszewska‐Olszowska, J., Serafin, Z., & Kazimierczak, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-32226-0 · Cited by 8

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Shahbazinia, A., Ponzina, F., Miranda, J., Dan, J., Ansaloni, G., & Atienza, D. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10781699 · Cited by 8

Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data

O’Shea, A., Li, M., Mercaldo, N. D., Balthazar, P., Som, A., Yeung, T., Succi, M. D., Little, B. P., Kalpathy–Cramer, J., & Lee, S. I. (2022) · BJR|Open · doi:10.1259/bjro.20210062 · Cited by 8

Radiologists’ views on artificial intelligence and the future of radiology: insights from a US National survey

Alarifi, M. (2025) · British Journal of Radiology · doi:10.1093/bjr/tqaf222 · Cited by 7

Probing an AI regression model for hand bone age determination using gradient-based saliency mapping

Wang, Z. (2021) · Scientific Reports · doi:10.1038/s41598-021-90157-y · Cited by 7

Quality use of artificial intelligence in medical imaging: What do radiologists need to know?

Goergen, S., Frazer, H. M. L., & Reddy, S. (2022) · Journal of Medical Imaging and Radiation Oncology · doi:10.1111/1754-9485.13379 · Cited by 7

Maturity Framework for Operationalizing Machine Learning Applications in Health Care: Scoping Review

Li, Y., Tian, J., Xu, A., Greiner, R., Hayward, J., Greenshaw, A. J., & Cao, B. (2025) · Journal of Medical Internet Research · doi:10.2196/66559 · Cited by 10

Reliability of AI-driven cephalometric analysis on CBCT: comparison with manual 2D cephalometry

Kazimierczak, N., Chwarścianek, N., Sultani, N., Janiszewska‐Olszowska, J., Jankowski, T., Nowicka, A., Serafin, Z., & Kazimierczak, W. (2026) · Progress in Orthodontics · doi:10.1186/s40510-026-00613-6 · Cited by 6

Exploring the Potential of Large Language Models in Radiological Imaging Systems: Improving User Interface Design and Functional Capabilities

Zhang, L., Shu, J., Hu, J., Li, F., He, J., Wang, P., & Shen, Y. (2024) · Electronics · doi:10.3390/electronics13112002 · Cited by 6

The Introduction of Artificial Intelligence in Diagnostic Radiology Curricula: a Text and Opinion Systematic Review

Santos, G. N. M., da Silva, H. E. C., Figueiredo, P. T. D. S., Mesquita, C. R. M., de Melo, N. S., Stefani, C. M., & Leite, A. F. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-022-00324-z · Cited by 9

Understanding Bias in Artificial Intelligence: A Practice Perspective

Davis, M. A., Wu, O., Ikuta, I., Jordan, J. E., Johnson, M. H., & Quigley, E. P. (2023) · American Journal of Neuroradiology · doi:10.3174/ajnr.a8070 · Cited by 6

Prospects for the use of artificial intelligence in dentistry

Doroshenko, O., Bida, V., Volosovets, T. M., Дорошенко, М. В., Omelianenko, O., Leonenko, P., Дорошенко, М. В., & Andrusenko, A. S. (2024) · SUCHASNA STOMATOLOHIYA · doi:10.33295/1992-576x-2024-5-72 · Cited by 5

Chat GPT-4 shows high agreement in MRI protocol selection compared to board-certified neuroradiologists

Bendella, Z., Wichtmann, B. D., Clauberg, R., Keil, V. C., Lehnen, N. C., Haase, R., Sáez, L. C., Wiest, I. C., Kather, J. N., Endler, C., et al. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112416 · Cited by 6

Rogue AI: Cautionary Cases in Neuroradiology and What We Can Learn From Them

Young, A., Tan, K., Tariq, F., Jin, M. X., & Bluestone, A. Y. (2024) · Cureus · doi:10.7759/cureus.56317 · Cited by 5

Revolutionizing electrocardiography: the role of artificial intelligence in modern cardiac diagnostics

Qayyum, S. N., Iftikhar, M., Rehan, M., Khan, G. A., Khan, M., Naeem, R., Ansari, R. S., Ullah, I., & Noori, S. (2024) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000002778 · Cited by 5

Evolution of an Artificial Intelligence-Powered Application for Mammography

Vasilev, Y. A., Андреевич, Р. Д., Vladzymyrskyy, A. V., Omelyanskaya, O. V., Дмитриевич, П. Л., Михайлович, Ш. И., Nikitin, E. D., Kapninskiy, A., & Arzamasov, K. M. (2025) · Diagnostics · doi:10.3390/diagnostics15070822 · Cited by 5

Challenges and Future Perspectives for Artificial Intelligence in Hepatology: Breaking Barriers for Better Care

Kusztos, V. E., & Simonetto, D. A. (2025) · Journal of Clinical and Experimental Hepatology · doi:10.1016/j.jceh.2025.102579 · Cited by 5

Mitigating catastrophic forgetting in Multiple sclerosis lesion segmentation using elastic weight consolidation

Álvarez, L., Valverde, S., Rovira, À., & Lladó, X. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103795 · Cited by 6

Artificial intelligence can overcome challenges in brachytherapy treatment planning

Jia, X., Cunha, J. A. M., & Rong, Y. (2022) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.13504 · Cited by 6

Pre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions

White, R., Demirer, M., Gupta, V., Sebro, R., Kusumoto, F. M., & Erdal, B. S. (2022) · Journal of medical imaging · doi:10.1117/1.jmi.9.5.054504 · Cited by 6

Challenges in Adopting Artificial Intelligence Based User Input Verification Framework in Reporting Software Systems

Kim, D. J., Locke, S., Chen, T.-H. P., Toma, A., Sporea, S., Weinkam, L., & Sajedi, S. (2023) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip58684.2023.00014 · Cited by 5

Digital Transformation in Health Care

Gersch, M., & Wessel, L. (2023) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190933463.013.19 · Cited by 5

"Usage of Artificial Intelligence in Gallbladder Segmentation to Diagnose Acute Cholecystitis"

Hong, Y. (2024) · Biomedical Journal of Scientific & Technical Research · doi:10.26717/bjstr.2024.55.008670 · Cited by 5

Unterstützung ärztlicher und pflegerischer Tätigkeit durch KI: Handlungsempfehlungen für eine verantwortbare Gestaltung und Nutzung

Bratan, T., Schneider, D., Funer, F., Heyen, N. B., Klausen, A. D., Liedtke, W., Lipprandt, M., Salloch, S., & Langanke, M. (2024) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-024-03918-1 · Cited by 5

AI and Medical Imaging: The Next Era of Radiology and Pathology Detection

Gupta, A., Gupta, A., Soanki, S., & Kishore, J. (2025) · International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT) · doi:10.1109/ce2ct64011.2025.10939755 · Cited by 5

Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics

Perkonigg, M., Hofmanninger, J., Herold, C., Prosch, H., & Langs, G. (2022) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2022-4g6b · Cited by 5

Guiding Antibiotic Therapy with Machine Learning: Real-World Applications of a CDSS in Bacteremia Management

de la Torre, J. C. G., Frenkel, A., Chavez-Lencinas, C., Rendon, A., Hueda, M., Higuchi, Y., Vela-Ruiz, J. M., Calpey, J., Beaton, R., Elijah, I., et al. (2025) · Life · doi:10.3390/life15111756 · Cited by 4

Continuous Machine Learning for Assisting AR Indoor Navigation

Kalathas, D., Koulouris, D., Menychtas, A., Tsanakas, P. D., & Maglogiannis, I. G. (2024) · SN Computer Science · doi:10.1007/s42979-024-03254-w · Cited by 4

Evaluating an Artificial Intelligence (AI) Model Designed for Education to Identify Its Accuracy: Establishing the Need for Continuous AI Model Updates

Verma, N., Getenet, S. T., Dann, C. E., & Shaik, T. (2025) · Education Sciences · doi:10.3390/educsci15040403 · Cited by 4

Artificial intelligence in globesity research: diagnosis, treatment, and prevention solutions for a healthier world with future recommendations

Munir, F., Chopra, H., Nasir, M. H., Simhachalam, L. V., Anis, Z. B., Bano, S., Islam, N., Baig, A. A., Heyat, M. B. B., Parveen, S., et al. (2025) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-025-02801-9 · Cited by 6

FetalMLOps: operationalizing machine learning models for standard fetal ultrasound plane classification

Testi, M., Fiorentino, M. C., Ballabio, M., Visani, G., Ciccozzi, M., Frontoni, E., Moccia, S., & Vessio, G. (2025) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-025-03436-5 · Cited by 4

Clinical AI in Radiology: Foundations, Trends, Applications, and Emerging Directions

Hartsock, I., Koutsoubis, N., Ahmed, S., Parker, N. H., Schabath, M. B., Araujo, C. R., Qayyum, A., Lam, C. A., Gatenby, R. A., & Rasool, G. (2026) · Cancers · doi:10.3390/cancers18060942 · Cited by 5

Performance changes due to differences among annotating radiologists for training data in computerized lesion detection

Nomura, Y., Hanaoka, S., Hayashi, N., Yoshikawa, T., Koshino, S., Sato, C., Tatsuta, M., Tanaka, Y., Kano, S., Nakaya, M., et al. (2024) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-024-03136-9 · Cited by 4

IMIL: Interactive Medical Image Learning Framework

Rao, A., Fisher, A. T., Chang, K., Panagides, J., McNamara, K. L., Lee, J.-Y., & Aalami, O. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00533 · Cited by 4

Using Optimal Feature Selection and Continuous Learning to Implement Efficient Model Arrays for Predicting Daily Clinical Radiology Workload

Lee, L. K., Viator, M., Giess, C. S., Gee, M. S., Huang, R., McPeake, F., & Pianykh, O. S. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.02.051 · Cited by 4

Creating a Diagnostic Assistance System for Diseases in Kampo Medicine

Koike, R., Ogawa‐Ochiai, K., Shirai, A., Hayashi, K., Arimitsu, J., Li, H., & Tsumura, N. (2021) · Applied Sciences · doi:10.3390/app11219716 · Cited by 4

Generalising from conventional pipelines using deep learning in high-throughput screening workflows

Cruz, B. G. S., Slter, J., Gomez‐Giro, G., Saraiva, C., Sabaté‐Soler, S., Modamio, J., Barmpa, K., Schwamborn, J. C., Hertel, F., Jarazo, J., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-15623-7 · Cited by 4

The optimal dynamic regret for smoothed online convex optimization with squared l2 norm switching costs

Zhang, Y., Liu, Q., Sun, J., & Wu, C. (2023) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2023.02.013 · Cited by 4

Multiparametric prostate MRI and structured reporting: benefits and challenges in the PI-RADS era

Mir-Bashiri, S., Yaqubi, K., Woźnicki, P., Westhoff, N., von Hardenberg, J., Huber, T. S., Froelich, M. F., Sommer, W. H., Reiser, M. F., Schoenberg, S. O., et al. (2021) · Chinese Journal of Academic Radiology · doi:10.1007/s42058-021-00059-1 · Cited by 4

Evaluating the Diagnostic Performance of Large Language Models in Identifying Complex Multisystemic Syndromes: A Comparative Study with Radiology ResidentsPreprint

Olshaker, H., Brin, D., Kalderon, E., Kraus, M., Konen, E., & Klang, E. (2024) · medRxiv · doi:10.1101/2024.06.05.24308335 · Cited by 4

Updates and advances for gynecologic imaging

Kitajima, K. (2025) · Journal of obstetrics and gynaecology research · doi:10.1111/jog.70025 · Cited by 3

Applications of artificial intelligence and generative artificial intelligence in digital healthcare ecosystem

Choudhury, R. R., & Roy, P. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-36434-1.00008-2 · Cited by 3

Robust Medical Image Prediction via Adaptive Reconstruction: Bridging the Gap in Low-Quality Data

Singhal, P., & Singh, M. (2026) · Journal of Informatics and Web Engineering · doi:10.33093/jiwe.2026.5.1.1 · Cited by 3

ACR’s Assess-AI: A Registry for Real-World Performance Monitoring of Clinical Imaging Artificial Intelligence

Coombs, L. P., Brink, L., Kim, W., Wald, C., Schmidt, K., Tilkin, M., Dreyer, K., Cook, T. S., & Bizzo, B. C. (2026) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2026.04.024 · Cited by 3

Awareness, perceptions, and educational preferences for AI among radiography students: A cross sectional survey

Oglat, A. A., AbuKhalil, T., Isawi, I. H., Alzyoud, K. S., Oqlat, A. A., Salman, M. D., Alnawafleh, T. M., & Bani-Ahmad, M. (2026) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2026.102452 · Cited by 3

Limitations of Artificial Intelligence in Orthodontics. Literature Review

Naureen, S. (2025) · Journal of Bahria University Medical and Dental College · doi:10.51985/jbumdc2024-452 · Cited by 3

Machine learning models in the prediction of chronic or shunt-dependent hydrocephalus following subarachnoid hemorrhage: A systematic review and meta-analysis

Hajikarimloo, B., Mohammadzadeh, I., Habibi, M. A., Tos, S. M., Asgarzadeh, A., Tajvidi, M., Aghajani, S., Hashemi, R., & Kooshki, A. (2025) · The Neuroradiology Journal · doi:10.1177/19714009251345104 · Cited by 3

Efficacy versus Effectiveness in Clinical Evaluation of Artificial Intelligence Algorithms for Medical Diagnosis: The Award Goes to Effectiveness

Herpe, G., Feydy, A., & d’Assignies, G. (2023) · Radiology · doi:10.1148/radiol.223132 · Cited by 3

A Conceptual Model for Labeling in Reinforcement Learning Systems: A Value Co-creation Perspective

Reinhard, P., Li, M. M., Dickhaut, E., Reh, C., Peters, C., & Leimeister, J. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-32808-4_8 · Cited by 3

Deep Learning-Based Detection of Glottis Segmentation Failures

Dadras, A. A., & Aichinger, P. (2024) · Bioengineering · doi:10.3390/bioengineering11050443 · Cited by 3

External COVID-19 Deep Learning Model Validation on ACR AI-LAB: It’s a Brave New World

Ardestani, A., Li, M., Chea, P., Wortman, J. R., Medina, A., Kalpathy–Cramer, J., & Wald, C. (2022) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2022.03.013 · Cited by 3

Opportunities and Challenges for Deep Learning in Brain Lesions

Patel, J., Chang, K., Ahmed, S. R., Jang, I., & Kalpathy–Cramer, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08999-2_2 · Cited by 3

Development of a mobile training app to assist radiographers’ diagnostic assessments

Toderis, L., Vo, A., Reychav, I., Sayeed, L., McHaney, R., & Guindy, M. (2022) · Health Informatics Journal · doi:10.1177/14604582221083780 · Cited by 3

The imperative of a radiology AI deployment registry and the potential of ChatGPT

Meşe, İ. (2023) · Clinical Radiology · doi:10.1016/j.crad.2023.04.001 · Cited by 3

Deep learning methods for point-of-care ultrasound examination

Ignesti, G., Deri, C., D’Angelo, G., Pratali, L., Bruno, A., Benassi, A., Salvetti, O., Moroni, D., & Martinelli, M. (2023) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis61268.2023.00078 · Cited by 2

Comparative Analysis of CNN and U-Net Models for Pneumonia Detection in Chest X-Ray Images

Fathan, M. R. S., Faturohman, M. F., Meiliana, M., & Zakiyyah, A. Y. (2024) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss62896.2024.10751403 · Cited by 2

Cascaded 3D Diffusion Models for Whole-Body 3D 18-F FDG PET/CT Synthesis from Demographics

Yoon, S., Song, S., Jin, P., Tivnan, M., Oh, Y., Kim, S., Wu, D., Li, X., & Li, Q. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04947-6_10 · Cited by 3

Radiologic Windchill

Behzad, S., Boucher, J. O., Alavijeh, N. S., Ravi, K., & Gholamrezanezhad, A. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.07.057 · Cited by 2

A Review of Current Applications of Artificial Intelligence and Machine Learning in Medical Science

Rathod, J. K. A., Bhavani, N., Saldanha, P. P., Rao, P. M., & Patil, P. (2021) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-2125 · Cited by 2

Towards Co-designing a Human-Agent Interface: A Case Study in Online Grooming Detection.

Daish, P., Micallef, N., Lorenzo-Dus, N., Paiement, A., & Sahoo, D. R. (2024) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3687272.3690893 · Cited by 2

AI for Predictive Healthcare: Real-Time Insight Generation from Diverse Patient Data

Singh, D. K., Bharti, K. K., Shahi, B., & Larhgotra, A. (2025) · International Conference on Data Science and Business Systems (ICDSBS) · doi:10.1109/icdsbs63635.2025.11031750 · Cited by 2

Vertrauen in KI-basierte Radiologie – Erste Erkenntnisse durch eine explorative Stakeholder-Konsultation

Gersch, M., Meske, C., Bunde, E., Aldoj, N., Wesche, J. S., Wilkens, U., & Dewey, M. (2021) · Forum Dienstleistungsmanagement · doi:10.1007/978-3-658-34326-2_12 · Cited by 2

Investigation on continual training of computer-aided diagnosis systems by semi-supervised learning

Muramamatsu, C., Nishio, M., Oiwa, M., Yakami, M., Kubo, T., & Fujita, H. (2022) · International Conference on Intelligent Medicine and Image Processing (IMIP) · doi:10.1145/3524086.3524095 · Cited by 2

The use of artificial intelligence in radiology: new possibilities for diagnostic imaging. A literature review

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Navigating the unknown: out-of-distribution detection for medical imaging

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