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Skip search results from other journals and go to results- 15 Journal of Medical Internet Research
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First, the high volume of screenings, combined with the requirement for independent, blinded double-reading by radiologists, places significant pressure on the existing radiology workforce [3]. Second, high false-positive recall rates on initial screening often lead to additional procedures and cause undue anxiety for the patient [4].
J Med Internet Res 2025;27:e62941
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collaboration with the European Hematology Association (EHA), the European Society for Therapeutic Radiologyradiology
JMIR Cancer 2025;11:e63964
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Ideally, radiology reports would combine the depth and flexibility of narrative information with the clarity and structure of laboratory reports, allowing for a quickly comprehensible, easy, and unambiguous use for referring physicians. However, the style of radiology reports reflects the conflicting priorities of coping with high-throughput and standardized processes in radiology departments and providing individualized and patient-centered diagnostic information [4].
J Med Internet Res 2025;27:e68427
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Can Artificial Intelligence Diagnose Knee Osteoarthritis?
Reference 2: The benefits of artificial intelligence in radiology: transforming healthcare through enhanced Reference 4: Patients' perceptions of using artificial intelligence (AI)-based technology to comprehend radiology Reference 6: Comparison of chest radiograph interpretations by artificial intelligence algorithm vs radiologyradiology
JMIR Biomed Eng 2025;10:e67481
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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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