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To develop an image retrieval pipeline capable of identifying specific series of thoracic aortic computed tomography (CT) scans from a diverse database.

No existing algorithm can reliably identify metastasis from pathology reports across multiple cancer types and the entire US population. In this study, we develop a deep learning model that automatically detects patients with metastatic cancer by using pathology reports from many laboratories and of multiple cancer types.

To assess the calibration of 9 large language models (LLMs) within biomedical natural language processing (BioNLP) tasks, furthering understanding of trustworthiness and reliability in real-world settings.

We aim to leverage more comprehensive phenotypic and genotypic clinical data to enhance the treatment response predictions.

To improve the identification of patients with health-related social needs (HRSNs) in the emergency department (ED), we developed and integrated a risk prediction score into an existing Fast Healthcare Interoperability Resources (FHIR)-based clinical decision support (CDS).

To determine whether a novel digital tool, the Community Vulnerability Compass (CVC), built using large datasets, can accurately measure neighborhood- and individual-level social determinants of health (SDOH) at scale. Existing SDOH indexes fall short of this dual requirement.

Acute kidney injury (AKI) is common in intensive care unit (ICU) patients and is associated with high mortality, prolonged ICU stays, and increased costs. Early prediction is crucial for timely intervention and improved outcomes. Various prediction models, including machine learning, deep learning, and dynamic prediction frameworks, have been developed, but their modeling approaches, data utilization, and clinical applicability require further investigation. This review comprehensively assesses the modeling methods, data utilization [...]

Conversational Health Agents (CHAs) are interactive systems providing healthcare services, such as assistance and diagnosis. Current CHAs, especially those utilizing Large Language Models (LLMs), primarily focus on conversation aspects. However, they offer limited agent capabilities, specifically needing more multistep problem-solving, personalized conversations, and multimodal data analysis. We aim to overcome these limitations.

To assess the effect of a digital scribe among pediatric providers on documentation time, cognitive load, burnout, and caregiver satisfaction.