Möglichkeiten der künstlichen Intelligenz im Strahlenschutz: Verbesserung der Sicherheit bei medizinischen Bildgebungsuntersuchungen

CLINICAL/METHODOLOGICAL ISSUE: Imaging of structures of internal organs often requires ionizing radiation, which is a health risk. Reducing the radiation dose can increase the image noise, which means that images provide less information. STANDARD RADIOLOGICAL METHODS: This problem is observed in commonly used medical imaging modalities such as computed tomography (CT), positron emission tomography (PET), single photon emission computed tomography (SPECT), angiography, fluoroscopy, and any modality that uses ionizing radiation for imaging. METHODOLOGICAL INNOVATIONS: Artificial intelligence (AI) can improve the quality of low-dose images and help minimize radiation exposure. Potential applications are explored, and frameworks and procedures are critically evaluated. PERFORMANCE: The performance of AI models varies. High-performance models could be used in clinical settings in the near future. Several challenges (e.g., quantitative accuracy, insufficient training data) must be addressed for optimal performance and widespread adoption of this technology in the field of medical imaging. PRACTICAL RECOMMENDATIONS: To fully realize the potential of AI and deep learning (DL) in medical imaging, research and development must be intensified. In particular, quality control of AI models must be ensured, and training and testing data must be uncorrelated and quality assured. With sufficient scientific validation and rigorous quality management, AI could contribute to the safe use of low-dose techniques in medical imaging.