Rapid advances in large language models (LLMs) are transforming the role of students and principal investigators (PIs) in biomedical research. This perspective examines how LLMs can reshape the laboratory model as de facto “Co-PIs”…
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Chest X-rays or chest radiography (CXR), commonly used for medical diagnostics, typically enables limited imaging compared to computed tomography (CT) scans, which offer more detailed and accurate three-dimensional data, particularly contrast-enhanced scans like CT…
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Physical inactivity contributes to chronic diseases globally. Digital behavior change interventions (DBCIs) offer scalable solutions, but previous meta-analyses often combined them with other interventions. This systematic review and meta-analysis evaluated the standalone effects of…
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Pathologic myopia is a leading cause of visual impairment and blindness. While deep learning-based approaches aid in recognizing pathologic myopia using color fundus photography, they often rely on implicit patterns that lack clinical interpretability….
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Accurate and comprehensive interpretation of pulmonary embolism (PE) from Computed Tomography Pulmonary Angiography (CTPA) scans remains a clinical challenge due to the limited specificity and structure of existing AI tools. We propose an agent-based…
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The market for digital health monitoring is expanding rapidly, with technologies that track health information and provide access to medical data promising benefits for users, particularly in areas with limited healthcare resources. To understand…
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Falls are a major cause of injuries among older people, with medication being a key risk factor. The SNOWDROP intervention introduces a clinical decision support system for general practitioners (GPs) offering personalized deprescribing advice,…
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We introduce a framework to adapt large language models for medicine: (1) Modeling: breaking down medical workflows into manageable steps; (2) Optimization: optimizing model performance via advanced adaptations; and (3) System engineering: developing agent…
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In healthcare, federated learning (FL) is emerging as a methodology to enable the analysis of large and disparate datasets while allowing custodians to retain sovereignty. While FL minimises data-sharing challenges, concerns surrounding ethics, privacy,…
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With the shift toward de-escalating surgery in breast cancer, prediction models incorporating imaging can reassess the need for surgical axillary staging. This study employed advancements in deep learning to comprehensively evaluate routine mammograms for…
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