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Deep-Learning-Based Computer-Aided Grading of Cervical Spinal Stenosis from MR Images: Accuracy and Clinical Alignment

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Artificial intelligence in forensic neuropathology: A systematic review

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Head-to-Head Comparison of 2 Artificial Intelligence Computer-Aided Triage Solutions for Detecting Intracranial Hemorrhage on Noncontrast Head CT

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Diagnostic performance of artificial intelligence for facial fracture detection: a systematic review

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A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification

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TL-LFF Net: transfer learning based lighter, faster, and frozen network for the detection of multi-scale mixed intracranial hemorrhages through genetic optimization algorithm

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Symmetry Awareness Encoded Deep Learning Framework for Brain Imaging Analysis

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See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning

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Zero-shot multi-modal large language models v.s. supervised deep learning: A comparative analysis on CT-based intracranial hemorrhage subtyping

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Automatic Alignment of Cranial CT Examinations to the Anterior Commissure/Posterior Commissure (ACPC) Reference Plane for Reliable Interpretation and Quality Assurance

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Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

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Labeling Noncontrast Head CT Reports for Common Findings Using Natural Language Processing

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Estimation of best corrected visual acuity based on deep neural network

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A detailed dosimetric comparative study of IMRT and VMAT in normal brain tissues for nasopharyngeal carcinoma patients treated with radiotherapy

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An Improved Mosaic Method for the Localization of Intracranial Hemorrhages Through Bounding Box

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Structure-Enhanced Unsupervised Domain Adaptation for CT Whole-Brain Segmentation

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Using multimodal ultrasound including full-time-series contrast-enhanced ultrasound cines for identifying the nature of thyroid nodules

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Better Application of Bayesian Deep Learning to Diagnose Disease

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An Explainable Multi-Instance Multi-Label Classification Model for Full Slice Brain CT Images

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Comparative Study of Deep Learning Models for Detection and Classification of Intracranial Hemorrhage

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Can artificial Intelligence Prediction Algorithms Exceed Statistical Predictions?

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Unsupervised Brain Abnormality Detection Using High Fidelity Image Reconstruction Networks.Preprint

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Effect of an Artificial Intelligence Chest X-Ray Disease Prediction System on the Radiological Education of Medical Students: A Pilot StudyPreprint

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Causal Effect Estimation on Imaging and Clinical Data for Treatment Decision Support of Aneurysmal Subarachnoid Hemorrhage

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Large-Scale Foundation Models for Radiological Image Analysis: Clinical Applications, Technical Challenges, and Future Directions

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ICHSC-Diff: A dual-stream guided conditional diffusion model for early hematoma expansion prediction

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FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising

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GEL-TTA Net: a Global ensemble learning network for the localization of small-scale and mixed intracranial hemorrhages through test time augmentations

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Automatic Implant Generation for Cranioplasty via Occupancy Networks

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Depth Sensing in AI on Exploring the Nuances of Decision Maps for Explainability

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Enabling Physicians to Make an Informed Adoption Decision on Artificial Intelligence Applications in Medical Imaging Diagnostics: Qualitative Study

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Multi-scale Superpixel based Hierarchical Attention model for brain CT classification

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Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care

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The Impact of Artificial Intelligence on the Preference of Radiology as a Future Specialty Among Medical Students at Jazan University, Saudi Arabia: A Cross-Sectional Study

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Interpretable 3D Multi-modal Residual Convolutional Neural Network for Mild Traumatic Brain Injury Diagnosis

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Mind the Clot: Automated LVO Detection on CTA using Deep Learning

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Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare Security

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Near-Infrared Autofluorescence Signature: A New Parameter for Intraoperative Assessment of Parathyroid Glands in Primary Hyperparathyroidism

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Unsupervised Acute Intracranial Hemorrhage Segmentation With Mixture Models

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Classification of Brain Hemorrhage Using Fine-Tuned Transfer Learning

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Agrawal, D. K., Poonamallee, L., & Joshi, S. (2023) · Indian Journal of Neurotrauma · doi:10.1055/s-0043-1770770 · Cited by 6

MSRL-Net: An Automatic Segmentation of Intracranial Hemorrhage for CT Images Based on the U-Net Framework

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Machine learning in action: Revolutionizing intracranial hematoma detection and patient transport decision-making

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Global research evolution and frontier analysis of artificial intelligence in brain injury: A bibliometric analysis

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Regression-Based Line Detection Network for Delineation of Largely Deformed Brain Midline

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A CNN-RNN based approach for Simultaneous Detection, Identification and Classification of Intracranial Hemorrhage

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Spotting brain bleeding after sparse training

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Machine learning applications using diffusion tensor imaging of human brain: A PubMed literature reviewPreprint

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Deep learning-based prediction for significant coronary artery stenosis on coronary computed tomography angiography in asymptomatic populations

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Development of a Clinically Applicable Deep Learning System Based on Sparse Training Data to Accurately Detect Acute Intracranial Hemorrhage from Non-enhanced Head Computed Tomography

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Enhancing the spatial resolution of neutron radiography with generative adversarial networks

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Using Machine Learning to Predict the Prognosis in Endometrial Cancer Patients Undergoing Fertility‐Sparing Treatment

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Anatomically-guided Masked Autoencoder with Domain-Adaptive Prompting (AMAP) for multimodal cerebral aneurysm detection and segmentation

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Multi-center–validated machine learning model for cervical cancer based on human papillomavirus genotyping results

Chen, Z., Zeng, Y., Luo, L., Li, J., Zheng, L., Xie, Y., Xiao, J., Zhang, P., Zhang, Q., Sun, Y., et al. (2025) · International Journal of Gynecological Cancer · doi:10.1016/j.ijgc.2025.102855 · Cited by 4

A comprehensive review of CT artifact reduction: From traditional reduction techniques to deep learning methods

Chen, K., Wang, Y., Ji, S., Meng, C., Pan, C., Tang, Z., & Chen, D. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102728 · Cited by 4

Chatting Ain’t Diagnosing: Diagnostic Variability and Fundamental Errors in Multimodal LLM Interpretation in Radiology

Hong, S., Matalia, M., & Toma, M. (2026) · Algorithms · doi:10.3390/a19030170 · Cited by 4

Machine learning in ARDS: an intensivist’s guide to artificial intelligence applications

Lombardi, R., Chaturvedi, M., Jozwiak, M., & Garg, M. (2026) · Critical Care · doi:10.1186/s13054-026-05928-8 · Cited by 4

Convolution Neural Network Model with Feature Linked Vector for Oral Cancer Detection

Krishna, P. S., Aparna, V. R., Priyanka, V., Niharika, P., & -, T. S. (2023) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt57126.2023.10134660 · Cited by 4

Pre-acquired CT-based attenuation correction with automated headrest removal for a brain-dedicated PET system

Iwao, Y., Akamatsu, G., Tashima, H., Takahashi, M., & Yamaya, T. (2023) · Radiological Physics and Technology · doi:10.1007/s12194-023-00744-z · Cited by 4

Current and Emerging Strategies for Tubo-Ovarian Cancer Diagnostics

Brincat, M., Mira, A. R., & Lawrence, A. C. (2023) · Diagnostics · doi:10.3390/diagnostics13213331 · Cited by 4

Multi-Stage Transformer Fusion for Efficient Intracranial Hemorrhage Subtype Classification

Wang, Y., Stefanidis, A., & Liu, J. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635323 · Cited by 4

Postmortem changes in porcine eyes on computed tomography images

Yoshimiya, M., Noriki, S., Shimbashi, S., Uesaka, H., & Hyodoh, H. H. (2025) · Legal Medicine · doi:10.1016/j.legalmed.2025.102568 · Cited by 4

Deep-learning tool for early identification of non-traumatic intracranial hemorrhage etiology and application in clinical diagnostics based on computed tomography (CT) scans

Zhao, M., Li, W., Hu, Y., Jiang, R., Zhao, Y., Zhang, D., Zhang, Y., Wang, R., Cao, Y., Zhang, Q., et al. (2025) · PeerJ · doi:10.7717/peerj.18850 · Cited by 4

Assessment of commercially available artificial intelligence software for differentiating hemorrhage from contrast on head CT following thrombolysis for ischemic stroke

Olsen, A. L., & Ginat, D. T. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1458142 · Cited by 4

Artificial intelligence and computed tomography imaging for midline shift detection

Gençtürk, T. H., Gülağız, F. K., & Kaya, İ. (2025) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-025-01779-6 · Cited by 4

Artificial Intelligence for Detecting Pulmonary Embolisms via CT: A Workflow-oriented Implementation

Abed, S., Hergan, K., Dörrenberg, J., Brandstetter, L., & Lauschmann, M. (2025) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056367860250630072749 · Cited by 4

Artificial intelligence in traumatic brain injury: Brain imaging analysis and outcome prediction: A mini review

Marino, L., & Bilotta, F. (2025) · World Journal of Critical Care Medicine · doi:10.5492/wjccm.v14.i3.107611 · Cited by 4

Introduction to Deep Learning in Clinical Neuroscience

de Dios, E., Ali, M. B., Gu, I. Y., Vecchio, T. G., Ge, C., & Jakola, A. S. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_11 · Cited by 4

Calibrating Histopathology Image Classifiers Using Label Smoothing

Wei, J., Torresani, L., Wei, J., & Hassanpour, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-09342-5_26 · Cited by 4

Künstliche Intelligenz in der Radiologie und Strahlentherapie aus der Perspektive von Ärzten und Medizinphysikexperten – Eine Interviewstudie

Kauffmann, A. L., Hennrich, J., Buck, C., & Eymann, T. (2022) · Springer eBooks · doi:10.1007/978-3-658-33597-7_29 · Cited by 4

A computer vision approach to identifying the manufacturer of posterior thoracolumbar instrumentation systems

Anand, A., Flores, Á., McDonald, M. F., Gadot, R., Xu, D. S., & Ropper, A. E. (2022) · Journal of Neurosurgery Spine · doi:10.3171/2022.11.spine221009 · Cited by 4

Machine learning technologies in CT-based diagnostics and classification of intracranial hemorrhages

Сморчкова, А. К., Khoruzhaya, A. N., Kremneva, E. I., & Petryaikin, A. V. (2023) · Burdenko s Journal of Neurosurgery · doi:10.17116/neiro20238702185 · Cited by 4

Deep Learning: Orthopaedic Research Evolves for the Future

Taunton, M. J., Liu, S. S., & Mont, M. A. (2023) · The Journal of Arthroplasty · doi:10.1016/j.arth.2023.08.070 · Cited by 4

Adaptive mask-based brain extraction method for head CT images

Hu, D., Qu, S., Jiang, Y., Han, C., Liang, H., & Zhang, Q. (2024) · PLoS ONE · doi:10.1371/journal.pone.0295536 · Cited by 4

Automated detection of intracranial hemorrhage in noncontrast head computed tomography

Vidya, M. S., Shastry, A., & Mallya, Y. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-820024-7.00004-9 · Cited by 4

Towards subject-level cerebral infarction classification of CT scans using convolutional networks

Schultheiß, M., Noël, P. B., Riederer, I., Thiele, F. O., Kopp, F. K., Renger, B. C., Rummeny, E. J., Pfeiffer, F., & Pfeiffer, D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235765 · Cited by 4

INTRACRANIAL HEMORRHAGE DETECTION IN CT SCAN USING DEEP LEARNING

Tharek, A., Muda, A. S., Hudi, A. B., & Huddin, A. B. (2022) · Asian Journal Of Medical Technology · doi:10.32896/ajmedtech.v2n1.1-18 · Cited by 42

Development of a Clinically Applicable Deep Learning System Based on Sparse Training Data to Accurately Detect Acute Intracranial Hemorrhage from Non-enhanced Head Computed Tomography

Wang, H.-C., Wang, S. H., Xiao, F., Ho, U., Lee, C. C., Yan, J., Chen, Y., & Ko, L.-W. (2025) · Neurologia medico-chirurgica · doi:10.2176/jns-nmc.2024-0163 · Cited by 5

Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning

Kuo, W., Häne, C., Mukherjee, P., Malik, J., & Yuh, E. L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1908021116 · Cited by 281

Automated Detection of Intracranial Hemorrhage from Head CT Scans Applying Deep Learning Techniques in Traumatic Brain Injuries: A Comparative Review

Agrawal, D. K., Poonamallee, L., & Joshi, S. (2023) · Indian Journal of Neurotrauma · doi:10.1055/s-0043-1770770 · Cited by 6

Deep learning-assisted detection of intracranial hemorrhage: validation and impact on reader performance

Kang, D.-W., Kim, M., Park, G.-H., Kim, Y., Han, M., Lee, M., Kim, D., Ryu, W., & Jeong, H. (2025) · Neuroradiology · doi:10.1007/s00234-025-03560-x · Cited by 18