Medical digital twins used to guide clinical interventions require validation that extends beyond conventional measures of predictive accuracy. A framework published in JAMIA Open
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Clinical prediction models can perform well on average while still making incorrect predictions with high confidence, creating risks when their outputs influence clinical decisions. BMC Me
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Value-based care depends on more than access to large volumes of information or increasingly sophisticated analytics. Progress is constrained when clinical, financial, utilisation and pati
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Cyber crisis planning is widespread, but operational disruption remains common when incidents occur. A global survey conducted in early 2025 covered 1,000 organisations across the UK, Fran
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Cybersecurity leaders in health are confronting faster, more sophisticated threats while managing persistent gaps in recovery, staffing and governance. A recent report draws on survey data f1
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Artificial intelligence models for predicting future cardiovascular disease risk show promising performance, but major validation and reporting gaps currently limit their clinical use. A s
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Ambient artificial intelligence documentation may reduce administrative pressure in emergency care while preserving physician oversight. A 12-month evaluation published in the Inte
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Large language models can classify patterns of usual interstitial pneumonia from radiology reports with substantial agreement against expert consensus, but their performance depends strong
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A report-guided artificial intelligence framework uses detailed descriptions of radiological abnormalities to locate and outline them on chest X-rays. Published in npj&nb
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Healthcare cyber resilience depends on restoring essential services quickly and limiting disruption to patient care when prevention fails. Evidence shows that ransomware disruption can pla
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