The integration of artificial intelligence into radiology promises to reduce workload and enhance diagnostic efficiency. However, despite an abundance of commercially available AI solutions
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Deep learning has transformed medical image segmentation, yet its performance often declines when applied to data from unfamiliar domains. This is especially problematic in abdominal imagin
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Locally advanced rectal cancer (LARC) continues to present challenges in treatment due to the high variability in patient outcomes following standard protocols. While total mesorectal excis
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Acute ischemic stroke (AIS) remains a major cause of death and long-term disability. Timely imaging and accurate analysis of brain lesions are critical to predicting functional recovery and
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Radiology and pathology reports are central to clinical decision-making and research, yet their unstructured format often hinders automated data analysis. While previous natural language pr
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As the integration of artificial intelligence (AI) in healthcare advances, there is increasing emphasis on the need for robust methodological standards in model development and validation.
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Artificial intelligence (AI) and deep learning have brought transformative potential to radiology, supporting diagnostics, workflow optimisation and triage. However, concerns regarding algo
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Magnetic resonance imaging (MRI) plays a vital role in diagnosing head and neck conditions. The region’s anatomical complexity, coupled with the prevalence of motion artefacts from breathin
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Radiology plays a critical role in diagnosing thoracic conditions, but its effectiveness depends heavily on the clarity and accuracy of reports. Unfortunately, various sources of error—rang
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Accurate molecular classification of endometrial cancer (EC) is pivotal for risk stratification, treatment decisions and outcome prediction. The 2023 FIGO staging guidelines underscore the
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