Latest AI and machine learning research in prescriptions for healthcare professionals.
Interest in large language models (LLMs) as a tool for meta-analyses and systematic reviews (MA/SRs) is growing. We prospectively developed 515 unique prompts by predefined screening-related categories and tested with open-access LLMs (Llama, Mistral) against four gold-standard MA/SRs from different medical fields published after the LLMs' training cut-offs, using a Python-based pipeline. Heteroge...
Artificial intelligence (AI) is being increasingly used in dermatology education and research as digital health data expands and large language models (LLMs) advance. This scoping review synthesized current applications, benefits, and limitations of AI in these domains. The review followed PRISMA-ScR methodology, including 102 studies published between 2010 and 2025, with 28 studies examining educ...
Programmed cell death pathways exacerbate secondary damage after spinal cord injury, yet their shared regulators and tractable therapeutic points rema...
In this study, we extend a previously introduced QM-AI strategy for predicting halogen···π interaction energies from a single aromatic model (represen...
BACKGROUND CONTEXT: Distinguishing malignant metastatic lesions from benign osteoporotic vertebral compression fractures (VCFs) is a major diagnostic ...
We present a novel strategy for the conversion of macrocyclic peptides into small molecules to identify potent and membrane-permeable protein-protein ...
OBJECTIVES: Artificial intelligence tools are transforming access to medication information. However, their ability to accurately identify antiretrovi...
Large language models (LLMs) mark a major development in artificial intelligence, with potentially transformative implications for ecology and conserv...
CONTEXT: Accurate prediction of drug-target affinity (DTA) is crucial for accelerating drug discovery, but it remains a significant challenge. While d...
BACKGROUND: Digital health tools integrating electronic patient-reported outcome and experience measures (ePROMs/ePREMs) enable longitudinal monitorin...
BACKGROUND: The increasing prevalence of patients with hyperpolypharmacy (> 10 medications) has made medication reviews increasingly complex. ChatGPT-...
Glioma is a highly malignant brain tumor with limited treatment options. We employed the Computational Analysis of Novel Drug Opportunities (CANDO) pl...
Selecting first-line antipsychotic medication for first episode of psychosis patients is a very challenging task requiring the clinicians to empirical...
Predicting drug indications is a fundamental task in biomedical research and drug repurposing. In addition to known therapeutic associations, clinical...
Emerging infectious diseases are one of the most significant threats to global health, driven by many factors such as zoonotic spillovers, climate cha...
Adverse drug reaction (ADR) prediction is typically formulated as drug-ADR association learning on extremely sparse, positive-unlabeled matrices, whic...
The global rise in the prevalence of obesity highlights the need for accessible and effective solutions for obesity management. ChatGPT, one of the fa...
MOTIVATION: Traditional drug discovery methods are costly and inefficient, while existing deep learning approaches remain limited by task specificity ...
Long QT Syndrome (LQTS) is an inherited cardiac disorder characterized by dysfunctional cardiac ion channels, which result in prolonged QT intervals o...
BACKGROUND: Visual identification and verification of medications during dispensing and administration are prone to human error, particularly in high-...