TY  - JOUR
AU  - Zhang, Subo
AU  - Zhu, Zhitao
AU  - Yu, Zhenfei
AU  - Sun, Haifeng
AU  - Sun, Yi
AU  - Huang, Hai
AU  - Xu, Lei
AU  - Wan, Jinxin
PY  - 2025
DA  - 2025/2/27
TI  - Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis
JO  - J Med Internet Res
SP  - e66622
VL  - 27
KW  - artificial intelligence
KW  - computed tomography
KW  - image quality
KW  - radiation protection
KW  - meta-analysis
AB  - Background: Artificial intelligence (AI) presents a promising approach to balancing high image quality with reduced radiation exposure in computed tomography (CT) imaging. Objective: This meta-analysis evaluates the effectiveness of AI in enhancing CT image quality and lowering radiation doses. Methods: A 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. Results: This 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. Conclusions: The 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2025/1/e66622
UR  - https://doi.org/10.2196/66622
DO  - 10.2196/66622
ID  - info:doi/10.2196/66622
ER  -