Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
Patient mobility monitoring in intensive care is critical for ensuring timely interventions and improving clinical outcomes. While accelerometry-based sensor data are widely adopted in training artificial intelligence models to estimate patient mobility, existing approaches face two key limitations highlighted in clinical practice: (1) modeling the long-term accelerometer data is challenging due...
This paper introduces the "IoT Integration Protocol for Enhanced Hospital Care", a comprehensive framework designed to leverage Internet of Things (IoT) technology to enhance patient care, improve operational efficiency, and ensure data security in hospital settings. With the growing emphasis on utilizing advanced technologies in healthcare, this protocol aims to harness the potential of IoT dev...
The integration of artificial intelligence (AI) into surgery raises significant ethical concerns, including the impact on autonomy, human authority an...
OBJECTIVE: Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) d...
BACKGROUND: There are significant disparities in outcomes at the end-of-life (EOL) for minoritized patients with advanced cancer, with most dying with...
OBJECTIVES: To implement a technology-based, systemwide readmission reduction initiative in a safety-net health system and evaluate clinical, care equ...
Chronic hepatitis B (CHB) infection represents a significant global public health issue, often leading to hepatitis B virus (HBV)-related liver cirrho...
Explainable AI (XAI) techniques are necessary to help clinicians make sense of AI predictions and integrate predictions into their decision-making w...
In this study, we investigate the feasibility of using a human-centered artificial intelligence (AI) chat platform where medical specialists collabo...
Grading student assignments in STEM courses is a laborious and repetitive task for tutors, often requiring a week to assess an entire class. For stu...
Background: Sepsis-Associated Acute Kidney Injury (SA-AKI) leads to high mortality in intensive care. This study develops machine learning models us...
Deploying natural language generation systems in clinical settings remains challenging despite advances in Large Language Models (LLMs), which conti...
Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesio...
Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding tr...
Sharing sensitive texts for scientific purposes requires appropriate techniques to protect the privacy of patients and healthcare personnel. Anonymi...
As Artificial Intelligence (AI) technologies become more integrated into clinical settings to optimize care, healthcare professionals (HCPs) will need...
Ocular surface and tear diseases are among the most common and significant ocular conditions affecting eye health. In recent years, research and clini...
Although the SARS-CoV-2 infection has established risk groups, identifying biomarkers for disease outcomes is still crucial to stratify patient risk a...
Background: Data collected in controlled settings typically results in high-quality datasets. However, in real-world applications, the quality of da...
Accurate and efficient electroencephalography (EEG) analysis is essential for detecting seizures and artifacts in long-term monitoring, with applica...