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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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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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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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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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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Medical education faces a scalability crisis, where rising class sizes strain individualized instruction, while students increasingly adopt unvalidated Generative AI (GenAI) tools for individualized learning support. This study investigated how medical students integrate constrained…
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AI is transforming healthcare, yet pediatric adoption remains limited and governance is underdeveloped. We review existing frameworks and identify pediatric-specific gaps: insufficient stakeholder engagement, developmentally appropriate consent/assent, limited bias mitigation, and unclear accountability. An…
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