Challenges exist with the adoption of Fast Healthcare Interoperability Resources (FHIR) within analytics, including the difficulty in transforming complex data structures, and performance issues when querying large datasets in their native JSON or XML…
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The Segment Anything Model (SAM) was fine-tuned on the EchoNet-Dynamic dataset and evaluated on external transthoracic echocardiography (TTE) and Point-of-Care Ultrasound (POCUS) datasets from CAMUS (University Hospital of St Etienne) and Mayo Clinic (99…
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In acute ischemic stroke due to large-vessel occlusion (AIS-LVO), late-window endovascular thrombectomy (EVT) decisions depend on penumbra-to-core (P:C) mismatch from computed tomographic perfusion (CTP). We developed multiple machine learning (ML) models to predict P:C…
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This paper proposes a “True Lifecycle Approach” (TLA) towards governing healthcare AI. The TLA governance model embeds core healthcare law principles—like informed consent, liability, and patient rights—throughout AI’s development, deployment, and use. Unlike narrow…
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Large language models (LLMs) are transforming the landscape of healthcare research, yet their role in qualitative analysis remains underexplored. This study compares human-led and LLM-assisted approaches to analyzing cancer patient narratives, using 33 semi-structured…
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Algorithmic bias occurs when predictive model performance varies meaningfully across sociodemographic classes, exacerbating systemic healthcare disparities. NYC Health + Hospitals, an urban safety net system, assessed bias in two binary classification models in our…
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Neurological conditions, including dementia, pose a major public health challenge, contributing to a significant and growing clinical, economic, and societal burden. Traditionally, research and clinical practice have focused on diseases like dementia in isolation….
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To evaluate the effectiveness of a digital medication system in improving adherence among patients with serious mental disorders (SMD), we conducted a cluster-randomized controlled trial across 30 communities in Beijing. Participants, aged 18–65 years,…
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Gambling harm is a global public health challenge. Gambling is often recorded in settings using routinely collected data (RCD). Linking of existing RCD affords numerous opportunities for policy-led research on gambling harm and early…
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Artificial intelligence (AI), particularly large language models (LLMs), is increasingly integrated into mental health care. This study examined racial bias in psychiatric diagnosis and treatment across four leading LLMs: Claude, ChatGPT, Gemini, and NewMes-15…
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