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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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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Large language models (LLMs) have shown promising capabilities across diverse domains, yet their application to complex clinical prediction tasks remains limited. In this study, we present CARE-AD (Collaborative Analysis and Risk Evaluation for Alzheimer’s…
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Preterm birth (PTB) significantly contributes to maternal and perinatal mortality and lifelong morbidity. While large language models (LLM) offer considerable potential for disease risk prediction and early detection, their application to PTB prediction using…
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Next-generation sequencing-based tests have advanced the field of medical diagnostics, but their novelty and cost can lead to uncertainty in clinical deployment. The Heme-STAMP is one such assay that tracks mutations in genes implicated…
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Oral potentially malignant disorder poses a significant risk of malignant transformation, particularly in cases with epithelial dysplasia (OED). Current OED assessment methods are invasive and lack reliable decision-support tools for cancer risk evaluation and…
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Accurate prediction of financial metrics in spine surgery is crucial as healthcare transitions to value-based care. While bundled payment models have succeeded in other orthopedic procedures, the heterogeneity of spinal surgery complicates their adoption….
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For patients with locally advanced cervical cancer (LACC), precise survival prediction models could guide personalized treatment. We developed and validated CerviPro, a deep learning-based multimodal prognostic model, to predict disease-free survival (DFS) in 1018…
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A study from the University of Westminster’s Research Centre for Optimal Health (ReCOH) has developed an AI method to predict the early onset of 38 age-related diseases through analysis of UK Biobank data.
The study,…
Biobanks are a rich source of data for genome-wide association studies (GWAS). They store clinical data from electronic health records, with data domains such as laboratory measurements, conditions, and self-reported diagnoses. Traditionally, biobank GWAS…
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