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Skip search results from other journals and go to results- 13 Journal of Medical Internet Research
- 9 JMIR Medical Informatics
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In this study, we conducted a systematic study to explore the capability of prompt-based LLMs in summarizing the impressions of various types of Chinese radiology reports using zero-shot and few-shot prompts. By leveraging automatic quantitative and clinical expert evaluations, we aim to clarify the current status of LLMs in Chinese radiology report impression summarization and the gap between the current achievements and requirements for application in clinical practice.
J Med Internet Res 2025;27:e65547
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One such model used in this study, known as the Radiology Common Data Model (R-CDM), standardizes imaging data, enabling the efficient integration of multi-institutional imaging and clinical data to enhance research capabilities [5].
Optical coherence tomography (OCT) captures detailed images of the eye’s internal structure, including parameters such as retinal thickness.
JMIR Med Inform 2025;13:e64422
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Reference 44: The role of child life in pediatric radiologyradiology
JMIR Serious Games 2025;13:e55720
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Studies have demonstrated the feasibility and potential of using artificial intelligence (AI) in medical decision-making, particularly in the radiology field [16,17]. For example, recent studies show the application of AI in cancer imaging analysis or in detecting acute intracranial hemorrhage on computed tomography (CT) or magnetic resonance imaging scans [18,19].
J Med Internet Res 2025;27:e48328
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To address the lack of studies measuring the improvement of diagnosis specificity in radiology using AI systems, this study aims to evaluate the impact of an AI-assisted lung nodule diagnostic system on the diagnostic accuracy of junior radiologists examining chest computed tomography (CT) scans. The results of this study could influence the future development of AI-assisted diagnostic systems to advance the accuracy of radiological diagnosis and treatment of lung nodules [22].
J Med Internet Res 2025;27:e64649
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In other words, perceptual errors in radiology are mistakes that occur during the visual inspection and interpretation of medical images. They are distinct from cognitive errors, which involve incorrect reasoning or decision-making based on observed information.
JMIR Form Res 2025;9:e53928
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These findings highlight a significant gap in the existing literature; there is a lack of comprehensive comparative studies that evaluate the performance of various LLMs across different diagnostic scenarios in radiology [19].
This study addresses this gap by comparing several mainstream LLMs in text-based radiology board exams, without imaging components, evaluating their overall performance. While a secondary objective is to analyze performance by question type and topic.
JMIR Med Educ 2025;11:e64284
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CNNs are a class of deep learning algorithms that recognize patterns in images, making them invaluable tools in radiology and other imaging-based diagnostics [2]. Numerous studies demonstrate CNNs’ effectiveness in medical imaging [3].
With advancements and developments in artificial intelligence (AI) technology, this research aims to evaluate the effectiveness of using Chat GPT-4 to detect pneumonia on x-ray images and compare its performance with specialized CNNs.
JMIR AI 2025;4:e67621
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AI in Dental Radiology—Improving the Efficiency of Reporting With ChatGPT: Comparative Study
To maintain the standard of patient care, it is, therefore, crucial to ensure high-quality training in radiology tasks during dental studies. Traditionally, radiology education involves manual interpretation of x-ray images and writing detailed medical findings reports based on visual inspection and clinical knowledge. However, the emergence of AI technologies has increased interest in alternative methods for radiology education and diagnostic reporting, including maxillofacial radiology [14,15].
J Med Internet Res 2024;26:e60684
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Current State of Community-Driven Radiological AI Deployment in Medical Imaging
Given the diverse nature of AI use cases in radiology, it is logical and helpful to categorize them for ease of discussion and analysis. These categories, however, should not be considered rigid or fixed, as the field of AI in radiology continues to evolve rapidly. This categorization approach allows for a more systematic exploration of AI deployment scenarios in radiology, facilitating knowledge sharing, collaboration, and the identification of common patterns or trends across different use cases.
JMIR AI 2024;3:e55833
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