What is it about?
Computed tomography (CT) scans are widely used to detect serious conditions such as lung cancer, strokes, and internal bleeding. However, reading large numbers of CT scans can be time-consuming and sometimes challenging for radiologists. This review explains how artificial intelligence (AI) is being used to help doctors analyze CT images more quickly and accurately. By reviewing published studies from the past 15 years, we found that AI systems can improve the detection of important findings, reduce the time needed to reach a diagnosis, and support doctors in making clinical decisions. AI was especially helpful in tasks like identifying lung nodules and urgent brain conditions. At the same time, the review highlights important challenges, including data bias, lack of transparency in AI decisions, and difficulties integrating AI into everyday clinical practice. Overall, the study shows that AI can be a powerful assistant in CT imaging when used carefully and responsibly.
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Why is it important?
This review is timely because AI tools are rapidly entering routine clinical radiology, yet many healthcare systems still lack clear guidance on their real-world performance and safe integration. Unlike many earlier reviews that focus only on technical accuracy, this work combines diagnostic performance, clinical decision-support impact, workflow integration, economic considerations, and regulatory challenges into a single framework. By synthesizing evidence across multiple clinical contexts, this study helps clinicians, researchers, and policymakers better understand where AI in CT imaging truly adds value, where risks remain, and what steps are needed for responsible deployment. This makes the review particularly relevant as regulatory bodies and hospitals worldwide are actively deciding how and when to adopt AI-based imaging tools.
Perspectives
Writing this article was particularly meaningful to me because it sits at the intersection of clinical medicine, technology, and patient safety. As AI adoption in radiology accelerates, I wanted to move beyond enthusiasm and provide a balanced, evidence-based overview that reflects both the promise and the limitations of these systems. My hope is that this work helps clinicians feel more informed and confident when engaging with AI tools, and encourages thoughtful implementation that keeps human judgment at the center of medical decision-making.
Dr. Kirolos Eskandar
Helwan University
Read the Original
This page is a summary of: Artificial Intelligence in CT Imaging: A Systematic Review of Diagnostic Accuracy, Clinical Decision–Support Impact, and Integration Pathways, iRADIOLOGY, December 2025, Tsinghua University Press,
DOI: 10.1002/ird3.70046.
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