Latest AI and machine learning research in prescriptions for healthcare professionals.
BACKGROUND AND AIM: Pharmacovigilance is essential to ensuring patient safety by enabling timely identification of adverse reactions in increasingly complex and voluminous data. Routine quantitative signal detection methods generate statistical alerts for product-event pairs based on predefined criteria; however, most alerts do not warrant further investigation, creating inefficiencies and signifi...
BACKGROUND: Health care systems generate vast amounts of unstructured text, such as clinical notes, which capture nuanced patient experiences, clinical reasoning, and subtle indicators of health status. While health system research has traditionally relied upon structured data, natural language processing (NLP) enables the extraction of this rich textual information. Leveraging NLP could improve t...
BACKGROUND: Adverse drug events (ADEs) pose significant public health challenges and economic burdens. While substantial ADE information is documented...
The increasing integration of artificial intelligence into learning environments has created new opportunities to examine how adaptive technologies in...
BACKGROUND: Real-world glucagon-like peptide-1 receptor agonist (GLP-1 RA) therapies face substantial attrition rates in commercial digital weight los...
Rapid urbanization has led to increasingly severe environmental degradation, necessitating the development of intelligent, adaptable, and explainable ...
OBJECTIVES: The aim of the present study was therefore to evaluate whether a programmed artificial intelligence (AI) system can reliably differentiate...
In this project, we investigate how visual complexity and the authorship of artworks, whether created by a human or modified by artificial intelligenc...
OBJECTIVE: Abdominal ultrasound is widely used for the routine screening of hepatobiliary and renal diseases because it is safe, inexpensive and broad...
Monitoring anti-drug antibody (ADA) responses is critical for evaluating the safety and efficacy of protein therapeutics. While traditional three-tier...
Postoperative acute kidney injury (PO-AKI) following noncardiac surgery remains a major clinical challenge, for which effective early warning models a...
OBJECTIVES: To 1) improve pharmacy students' perceived confidence in communication skills and 2) identify perceptions of counseling a text-based AI-si...
BACKGROUND: Breastfeeding duration is influenced by a complex interplay of obstetric, physiological, and sociodemographic factors. Traditional linear ...
Risk prediction tools assist pharmacists to identify and prioritise hospitalised patients at risk of drug-related problems (DRPs) requiring clinical r...
OBJECTIVES: This study aimed to use a propensity score matching (PSM) design to examine the association between artificial intelligence (AI)-driven co...
Corneal drug permeability is a key determinant of topical ocular drug delivery efficiency, yet its quantitative prediction remains challenging due to ...
BACKGROUND: Schistocytes are critical morphological markers for thrombotic microangiopathy (TMA) diagnosis. Manual identification is hampered by incon...
Drug target interaction (DTI) prediction is a critical task in drug discovery, as it has the potential to accelerate the identification of promising d...
Meckel's Pediatric drug-resistant epilepsy (DRE) is a significant neurological disorder that develops when seizures persist despite treatment with two...
BACKGROUND: Methanol poisoning poses significant challenges due to its rapid progression and high mortality rate, necessitating timely and accurate IC...