SynthesisJournal of medical Internet research2025
Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis.
Synthesis in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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.
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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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Role of Artificial Intelligence in the Characterization and Outcome Prediction of Prostate Cancer: A Systematic Review.Tomography (Ann Arbor, Mich.) · 2026Pooled it
- Performance of Commercial Deep Learning-Based Radiation Dose Optimization Software in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2026Review
- Comments on "Refining the Analytical Framework Linking CT Utilization and Pneumonia Incidence in Older Adults".Korean journal of radiology · 2026Article
- Feasibility of Artificial Intelligence-Processed Low-Dose Cone-Beam Computed Tomography in Dental Imaging.Bioengineering (Basel, Switzerland) · 2026Article
- Assessment of Fractional Flow Reserve from Coronary CT Angiography Using a Deep Learning-Based Algorithm: A Multicenter Retrospective Study.Diagnostics (Basel, Switzerland) · 2026Article
- Effects of Image Degradation on Deep Neural Network Classification of Scaphoid Fracture Radiographs: Comparison Study of Different Noise Types.JMIR medical informatics · 2026Article
- Vision-based diagnostic gain of ChatGPT-5 and gemini 2.5 pro compared with human experts in oral lesion assessment.Scientific reports · 2025Article
- Revolutionizing emergency care: an overview of the transformative role of artificial intelligence in diagnosis, triage, and patient management.International journal of emergency medicine · 2025Review
- Quantitative Evaluation of Low-Dose CT Image Quality Using Deep Learning Reconstruction: A Comparative Study of Philips Precise Image and GE TrueFidelity.Journal of imaging · 2025Article
- Molecular and Cellular Effects of CT Scans in Human Adipose Mesenchymal Stem Cells.International journal of molecular sciences · 2025Article
- Artificial intelligence for healthcare: restrained development despite impressive applications.Infectious diseases of poverty · 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) presents a promising approach to balancing high image quality with reduced radiation exposure in computed tomography (CT) imaging.
objectiveThis meta-analysis evaluates the effectiveness of AI in enhancing CT image quality and lowering radiation doses.
methodsA thorough literature search was performed across several databases, including PubMed, Embase, Web of Science, Science Direct, and Cochrane Library, with the final update in 2024. We included studies that compared AI-based interventions to conventional CT techniques. The quality of these studies was assessed using the Newcastle-Ottawa Scale. Random effect models were used to pool results, and heterogeneity was measured using the I² statistic. Primary outcomes included image quality, CT dose index, and diagnostic accuracy.
resultsThis meta-analysis incorporated 5 clinical validation studies published between 2022 and 2024, totaling 929 participants. Results indicated that AI-based interventions significantly improved image quality (mean difference 0.70, 95% CI 0.43-0.96; P<.001) and showed a positive trend in reducing the CT dose index, though not statistically significant (mean difference 0.47, 95% CI -0.21 to 1.15; P=.18). AI also enhanced image analysis efficiency (odds ratio 1.57, 95% CI 1.08-2.27; P=.02) and demonstrated high accuracy and sensitivity in detecting intracranial aneurysms, with low-dose CT using AI reconstruction showing noninferiority for liver lesion detection.
conclusionsThe findings suggest that AI-based interventions can significantly enhance CT imaging practices by improving image quality and potentially reducing radiation doses, which may lead to better diagnostic accuracy and patient safety. However, these results should be interpreted with caution due to the limited number of studies and the variability in AI algorithms. Further research is needed to clarify AI's impact on radiation reduction and to establish clinical standards.
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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 OpenQuestion graph.