Accurately determining progression in early-stage lung adenocarcinoma (esLUAD) is central to diagnosis, treatment selection and follow-up, yet conventional grading can m
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Automated reading of chest x-rays (CXR) using computer-aided detection (CAD) is recommended for adults in tuberculosis (TB) screening and triage, yet paediatric use remains unaddressed. Chi
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Personalised breast cancer screening relies on accurate near-term risk assessment to balance early detection with the harms of overdiagnosis. Interval cancers (ICs) that 
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Referring physicians who rely on radiology services are central to how artificial intelligence is adopted in clinical pathways. A survey of licensed doctors in Germany explored how the
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Rising MRI volumes and increasingly complex examinations continue to pressure radiology services. Responding to this demand, researchers developed a deep learning (DL) model to analyse rout
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Hepatocellular carcinoma (HCC) continues to drive significant morbidity and mortality. Microvascular invasion (MVI) is a key marker of aggressive biology, yet confirmation usually arrives o
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Artificial intelligence–generated content (AIGC) is advancing rapidly across nuclear medicine imaging (NMI), promising software-based gains in denoising, motion correction, attenuation 
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Artificial intelligence is accelerating progress across biotechnology and digital medicine by linking genomic, clinical and imaging data to generate more comprehensive insights th
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Healthcare teams face growing administrative pressure and shrinking time for patient interaction. Conversational agents powered by generative artificial intelligence are being positioned to
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Behavioural health services face sustained pressure from administrative burden, variable assessments and rising burnout. Interest in artificial intelligence has grown as providers look for
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