Latest AI and machine learning research in prescriptions for healthcare professionals.
BACKGROUND: Research on artificial intelligence (AI) and mental health has focused largely on harms at deployment, including chatbot safety, sycophancy, and AI-associated delusions. Less attention has been paid to a prior question: whether the human-generated text and preference judgments that shape large language models (LLMs) are themselves clinically reliable, particularly when self-report may ...
Elucidating compound-protein interactions is crucial for early drug discovery, offering insights into molecular mechanisms and therapeutic potential. While wet-lab methods detect interactions, they suffer from false positives, high costs, and labor intensity. Consequently, there is an urgent need to develop theoretical computational approaches for identifying interactions between compounds and pro...
BACKGROUND: Opioid overdose remains a leading cause of preventable death in the United States. Existing approaches to identify individuals at elevated...
AIM: To develop and validate a deep learning-based AI system for the dynamic, real-time differentiation of benign and malignant gastric ulcers during ...
BACKGROUND: Machine Learning (ML) models have achieved outstanding performance in predicting post-surgical survival. However, the "black-box" nature o...
Medication use during adolescence provides important insight into current health and treatment patterns. However, these data are often difficult to an...
OBJECTIVE: To develop and evaluate an automatic patient review analyzer that applies advanced Natural Language Processing (NLP) and machine learning m...
PURPOSE OF REVIEW: Tobacco use remains the leading preventable cause of death worldwide, while the rise of electronic nicotine products has sparked a ...
The modernization of traditional food products faces a critical challenge: generic packaging often fails to convey historical value, thereby reducing ...
Navigating the vast chemical space to identify potent therapeutic agents with optimal pharmacokinetic properties remains a formidable bottleneck in ph...
This study investigates the identification of Benign Prostatic Hyperplasia (BPH) through a deep learning-based analysis of RGB prostate histopathologi...
BACKGROUND: Long-term opioid therapy (LTOT) after hip fracture surgery is a common postoperative complication associated with adverse outcomes, yet to...
Drug-target interaction represents a critical focus area in computational drug discovery and pharmaceutical research. However, the process of identify...
BACKGROUND: Nursing education faces challenges in providing nursing students with sufficient clinical site opportunities due to healthcare staffing sh...
Artificial intelligence (AI) is transforming pediatric healthcare, offering novel opportunities for early diagnosis, personalized treatment, and more ...
OBJECTIVE: To compare accuracy, precision, recall, F1 and time spent using commercial tools to identify physiotherapy trials based on title and abstra...
BACKGROUND: While new expensive medicines often offer substantial benefits to patients, they can carry inherent drawbacks such as uncertainty regardin...
BACKGROUND: Tuberculosis-diabetes mellitus (TB-DM) multimorbidity significantly increases the risk of multidrug-resistant/rifampicin-resistant tubercu...
BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease. Traditional models for estimating AD onset cannot capture nonlinear i...
Medication-related problems (MRPs) place a substantial burden on the healthcare system, contributing to hospital admissions and significant healthcare...