We propose metrics from information theory to characterize the clinical and operational dynamics of hospitals. Ergodicity, the equality of time- and space-averages within a dynamical system, ensures its stationarity. Surprisal, the log-inverse of probability,…
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Grade 2 subclinical hypothyroidism (G2SCH) is associated with an increased risk of various diseases but is rarely detected before symptom onset. This study aims to develop a machine learning-based prediction model for G2SCH using…
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Tricuspid regurgitation (TR) remains underdiagnosed due to the lack of effective screening tools. We developed a self-distilled and pre-trained transformer model for detecting TR (SPEED-TR) from electrocardiography. The model was trained using 466,149 electrocardiogram-echocardiogram…
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Radiolucent foreign body aspiration (FBA) remains diagnostically challenging due to its subtle imaging signatures on chest CT scans, often leading to delayed or missed diagnoses. We present a deep learning model integrating MedpSeg, a…
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With a growing number of studies applying generative artificial intelligence (GAI) models for health purposes, reporting standards are being developed to guide authors in this space. We describe the currently available reporting guidelines that…
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Current self-supervised learning (SSL) methods for 3D medical imaging rely on simple pretext formulations and organ- or modality-specific datasets, limiting their generalizability and scalability. We present 3DINO, a cutting-edge SSL method adapted to 3D…
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Measures of healthcare quality and equity often overlook when care is delivered, potentially masking important disparities. We present a novel, time-aware approach to detect racial inequities in hospice use among 100,480 Medicare beneficiaries with…
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Cardiovascular diseases remain the leading cause of death in developed countries. This study introduces deep learning ensembles for arrhythmia detection and atrial fibrillation (AF) recurrence prediction from electrocardiogram scans, supported by explainable artificial intelligence…
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Electronic Health Records (EHRs) contain vast clinical data that are difficult for providers to synthesize. Generative AI with Large Language Models (LLMs) can summarize records to reduce cognitive burden, but ensuring accuracy requires reliable…
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Biased and poorly documented dermatology datasets pose risks to the development of safe and generalizable artificial intelligence (AI) tools. We created a Dataset Nutrition Label (DNL) for multiple dermatology datasets to support transparent and…
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