Identification of cohorts at higher risk of cancer can enable earlier diagnosis of the disease, which significantly improves patient outcomes. In this study, we select nine cancer sites with high incidence of late-stage diagnosis…
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We investigate issues pertaining to algorithmic fairness and digital health equity within the context of using machine learning to predict Parkinson’s Disease (PD) with data recorded from structured assessments of finger and hand movements….
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Timely care in a specialised neuro-intensive therapy unit (ITU) reduces mortality and hospital stays. Planned admissions to ITU following surgery are safer than unplanned ones. However, post-operative care decisions remain subjective. This study used…
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This systematic review examines the cost-effectiveness, utility, and budget impact of clinical artificial intelligence (AI) interventions across diverse healthcare settings. Nineteen studies spanning oncology, cardiology, ophthalmology, and infectious diseases demonstrate that AI improves diagnostic…
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Opioids are frequently overprescribed after surgery. We applied a tabular foundation model to predict the risk of post-discharge opioid consumption. The model was trained and internally validated on an 80:20 training/test split of the…
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A retrospective, registry-based cost-minimization analysis assessing whether initiating minor acute primary care episodes via a digital-first pathway reduces costs compared to traditional care in a Finnish setting. Of 637,923 encounters, 64,969 eligible episodes were…
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Digital twins offer a promising approach to advancing healthcare by providing precise, noninvasive monitoring and early detection of diseases. In heart failure (HF), a leading cause of mortality worldwide, they can improve patient monitoring…
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Approximately 50 million people suffer from sepsis yearly, and 13 million die from it. For every hour a patient with septic shock is untreated, their survival rate decreases by 8%. Therefore, rapid detection and…
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Many rare genetic diseases have recognizable facial phenotypes that serve as diagnostic clues. While Large Language Models (LLMs) have shown potential in healthcare, their application to rare genetic diseases still faces challenges like hallucination…
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Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening condition, and accurate prediction of functional outcomes is critical for optimizing patient management within the initial 3 days of presentation. However, existing clinical scoring systems and imaging…
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