ReviewMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2023
Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.
Review in Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 31 citations in OpenAlex.
- Enhanced image quality in head and neck MRI with PROPELLER and deep learning reconstruction.European journal of radiology open · 2026Article
- Recent Advances in Musculoskeletal Radiology: Bridging Innovation and Clinical Application.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Review
- Quality of Head and Neck Diffusion-weighted MR Imaging Using a Combination of the Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) Sequence and Deep Learning Reconstruction.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- CT-based texture analysis for preoperative prediction of key histopathological features in laryngeal cancer.Polish journal of radiology · 2026Article
- High Resolution TOF-MRA Using Compressed Sensing-based Deep Learning Image Reconstruction for the Visualization of Lenticulostriate Arteries: A Preliminary Study.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Review
- Dual-type deep learning-based image reconstruction for advanced denoising and super-resolution processing in head and neck T2-weighted imaging.Japanese journal of radiology · 2025Article
- Segmentation of airways and soft tissues on panoramic radiographs using artificial intelligence technology.BMC oral health · 2025Article
- Insights Into the Future: Assessing Medical Students' Artificial Intelligence Readiness - A Cross-Sectional Study at Kerman University of Medical Sciences (2022).Health science reports · 2025Article
- JJR-TOP GUN Phase 1, Year 2: new perspectives through the integration of artificial intelligence and radiology.Japanese journal of radiology · 2025Article
- Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects.Japanese journal of radiology · 2025Review
- Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.Japanese journal of radiology · 2025Review
- Review
- Magnetic resonance imaging in oral and maxillofacial surgery: a structured guide for trainees.Polish journal of radiology · 2025Review
- Data set terminology of deep learning in medicine: a historical review and recommendation.Japanese journal of radiology · 2024Review
- Overview of Target-Oriented Project Group United for Nippon (TOP GUN): fostering interdisciplinary collaboration among young researchers in radiology on timely topics.Japanese journal of radiology · 2024Article
- Recent trends in AI applications for pelvic MRI: a comprehensive review.La Radiologia medica · 2024Review
- AI Use in Mammography for Diagnosing Metachronous Contralateral Breast Cancer.Journal of imaging · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 14 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.