Emergency departments managing traumatic brain injury (TBI) must rapidly identify patients at highest risk of in-hospital death using limited early information. Widely used clinical measure
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Cloud services and artificial intelligence (AI) are increasingly embedded in healthcare delivery and operations. Patients schedule appointments, complete pre-check-ins and access lab result
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Accurate prognostication in prostate cancer is central to treatment planning and patient counselling. Histological assessment with the Gleason grading system has long provided a common lang
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Growing data availability has accelerated research and development, yet healthcare remains constrained by privacy and confidentiality requirements that limit the use of patient information.
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Long-term success after kidney transplantation depends on recognising evolving risk as health trajectories change. Models based only on pre-transplant characteristics can miss complications
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Systemic mastocytosis (SM) presents with heterogeneous, multisystem symptoms that can complicate recognition and delay diagnosis. Routine clinical documentation captures these signals uneve
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Clinical decision instruments are widely used to support diagnosis, prognosis and treatment decisions by translating clinical data into structured guidance. Their growing role reflects a br
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Large language model (LLM) chatbots are becoming commonplace in clinical contexts, including for generating differential diagnoses. Outputs can vary depending on how users frame and structure in1
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According to McKinsey’s Global Survey on the state of AI, artificial intelligence is now widely used across organisations, yet translating adoption into enterprise-wide impact remains chall
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Medical image analysis has achieved strong performance through deep learning, yet these gains often prove fragile when models encounter data that differ from their original training sets. I
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