ReviewJournal of imaging2025
A Systematic Review of Medical Image Quality Assessment.
Review in Journal of imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Photon-counting CT versus energy-integrating detectors for cardiac imaging: a systematic review of evidence from in vivo human studies on image quality and radiation dose.BMC medical imaging · 2025Pooled it
- U-Net-Based Automated Quality Control of Knee Radiographs: Dual-Center Validation and Clinical Intervention.Journal of imaging informatics in medicine · 2026Article
- Photo-Based Color Analysis in Restorative Dentistry: The Role of Artificial Intelligence Algorithms.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2026Article
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Labeled dataset of Sentinel-1 SAR imagery Despeckled with multitemporal fusions.Data in brief · 2026Article
- Image-Quality-Aware Multimodal Artificial Intelligence for Automated Structured OCT Report Generation in Glaucoma Evaluation.Ophthalmology science · 2026Article
- Design and Preliminary Phantom Study of a 3D-Printed Wrist Immobilization Device for Lateral Radiography.Diagnostics (Basel, Switzerland) · 2026Article
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- Robust image quality evaluation in optical coherence tomography of skin using global, region-independent metrics.Scientific reports · 2026Article
- Brain tumor classification using fractional Laplacian image enhancement and vision transformer.Scientific reports · 2026Article
- Efficient Two-Stage Autofocus for Micro-Assembly Based on Joint Spatial-Frequency Image Quality Assessment.Journal of imaging · 2026Article
- Context-specific image quality assessment for virtual histologic staining: checklist and guideline.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- HCLmNet: A unified hybrid continual learning strategy multimodal network for lung cancer survival prediction.PloS one · 2026Article
- Deep learning-based no-reference image quality assessment framework for Cryptosporidium spp. and Giardia spp.PloS one · 2026Article
- Explainable Radiomics-Based Model for Automatic Image Quality Assessment in Breast Cancer DCE MRI Data.Journal of imaging · 2025Article
- Subtalar Arthroereisis with Calcaneus Stop Screws-Can the Angles on Pre- and Post-Surgical X-Ray Images Be Reliably Measured by Artificial Intelligence?Children (Basel, Switzerland) · 2025Article
- Deep learning-based no-reference quality assessment of anterior segment ultrasound biomicroscopy panoramic images.Quantitative imaging in medicine and surgery · 2025Article
- No Reproducibility, No Progress: Rethinking CT Benchmarking.Journal of imaging · 2025Article
- Glaucoma Detection and Structured OCT Report Generation via a Fine-tuned Multimodal Large Language Model.ArXiv · 2025Article
- DFCNet: Dual-Stage Frequency-Domain Calibration Network for Low-Light Image Enhancement.Journal of imaging · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical image quality assessment (MIQA) is vital in medical imaging and directly affects diagnosis, patient treatment, and general clinical results. Accurate and high-quality imaging is necessary to make accurate diagnoses, efficiently design treatments, and consistently monitor diseases. This review summarizes forty-two research studies on diverse MIQA approaches and their effects on performance in diagnostics, patient results, and efficiency in the process. It contrasts subjective (manual assessment) and objective (rule-driven) evaluation methods, underscores the growing promise of machine intelligence and machine learning (ML) in MIQA automation, and describes the existing MIQA challenges. AI-powered tools are revolutionizing MIQA with automated quality checks, noise reduction, and artifact removal, producing consistent and reliable imaging evaluation. Enhanced image quality is demonstrated in every examination to improve diagnostic precision and support decision making in the clinic. However, challenges still exist, such as variability in quality and variability in human ratings and small datasets hindering standardization. These must be addressed with better-quality data, low-cost labeling, and standardization. Ultimately, this paper reinforces the need for high-quality medical imaging and the potential of MIQA with the power of AI. It is crucial to advance research in this area to advance healthcare.
Indexed as
Identifiers
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.