Generating synthetic data from medical records is a complex task intensified by patient privacy concerns. In recent years, multiple approaches have been reported for the generation of synthetic data, however, limited attention was given…
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This randomized, controlled trial evaluated the impact of plaque visualization combined with daily tasks on cardiovascular risk profile and included 240 participants with coronary arterial disease. The intervention group received the PreventiPlaque app during…
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Glaucoma is the leading cause of irreversible blindness globally. Research indicates a disproportionate impact of glaucoma on racial and ethnic minorities. Existing deep learning models for glaucoma detection might not achieve equitable performance across…
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While drug combinations are increasingly important in disease treatment, predicting their therapeutic interactions remains challenging. This review systematically analyzes computational methods for predicting drug combination effects through multi-omics data integration. We comprehensively assess key…
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Large language models (LLMs) with retrieval-augmented generation (RAG) have improved information extraction over previous methods, yet their reliance on embeddings often leads to inefficient retrieval. We introduce CLinical Entity Augmented Retrieval (CLEAR), a RAG…
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There is a growing need to evaluate the agreement between the field methods and integrate artificial intelligence (AI) using two-dimensional (2D) photos for enhanced real-world analysis. This study evaluated the agreement between AI-2D photos…
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Creating an ontology is the essential step in natural language processing (NLP). To improve patient safety in this era of generative AI, it is crucial to develop a standards-driven, ontology-based architecture for patient safety…
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Digital twins in precision medicine provide tailored health recommendations by simulating patient-specific trajectories and interventions. We examine the critical role of Verification, Validation, and Uncertainty Quantification (VVUQ) for digital twins in ensuring safety and…
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Extensive research on retinal layer segmentation (RLS) using deep learning (DL) is mostly approaching a performance plateau, primarily due to reliance on structural information alone. To address the present situation, we conduct the first…
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While machine learning (ML)-based solutions—often referred to as artificial intelligence (AI) solutions—have demonstrated comparable or superior performance to human experts across various healthcare applications, their vulnerability to perturbations and stability to variations due to…
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