Prescriptions

Artificial intelligence-driven study selection in systematic reviews of randomized controlled trials, emulated trials and economic evaluation studies using large language models.

Systematic reviews (SRs) are key to evidence-based medicine but are often labor-intensive, especially in the study selection step. This study assessed the use of large language models (LLMs) to automate SR study screening and selection. Five SR projects were included: two published therapeutic SRs (SR1-2), two ongoing emulated-trial SRs (SR3-4), and one economic evaluation SR (SR5). The total numb...

Perceived communication quality of a standardized neuroradiology report following AI-based simplification: a randomized, blinded study.

OBJECTIVES: Radiology reports are primarily written for professional communication and may be difficult for patients to understand. We investigated whether AI-based simplification of a single standardized neuroradiology report improves participant-rated communication quality compared with a conventional professional-language report. MATERIALS AND METHODS: In this prospective, randomized, blinded s...

iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data.

Precise resolution of cellular heterogeneity within complex tissues is fundamental to deciphering disease etiologies from bulk transcriptomic profiles...

Multitask learning in predictive toxicology: methods, benchmarks, and applications.

INTRODUCTION: Drug safety evaluation is inherently multi-endpoint, multi-scale, and mechanistically interconnected. Traditional predictive toxicology ...

Therapeutic hybrid intelligence with neural and knowledge-based expert reasoning for SRS (THINKERS): an AI model for lung cancer brain metastases.

BACKGROUND: Prescription dose selection for lung brain metastases treated with stereotactic radiosurgery (SRS) remains largely guided by generalized p...

Artificial Intelligence in Pharmacovigilance: Guiding Principles from the CIOMS Working Group XIV.

Artificial intelligence (AI) is increasingly being explored to support pharmacovigilance activities including processes involving individual case safe...

Intelligent Medication Recommendation via Dynamic Prescription Modeling and Molecular Substructure Learning.

Medication recommendation system is a critical application of artificial intelligence in healthcare, supporting clinicians in prescribing effective an...

Large Language Models as a New Tool for Therapists in Internet-Based Cognitive Behavioral Therapy: Blinded Clinician Rating Pilot Experiment.

BACKGROUND: Internet-based cognitive behavioral therapy (iCBT) is an effective and scalable alternative to face-to-face psychotherapy, but its reach i...

PAC-Net: A physics-guided multimodal hybrid network for antibody-antigen interaction prediction.

The precise prediction of Antibody-Antigen Interaction (AAI) is a pivotal task for accelerating antibody drug discovery and virtual screening. To addr...

MolGraphGAN: a graph transformer-adversarial framework for target-specific molecule generation.

The synthesis of small molecule target-specific compounds remains challenging due to the combinatorial complexity of chemical space and the limited in...

An Explainable Computer-Aided Framework for Skin Lesion Classification Using Deep Learning.

The utilization of deep convolutional neural networks for the purpose of diagnosing diseases in the skin area has proven to yield similar accuracy lev...

AI-driven drug design: a comprehensive review.

The integration of AI into drug design has undergone a transformative evolution, reshaping the landscape of medicinal chemistry. AI's exceptional capa...

Clinical Specialty Expansion of AI-Enabled and Machine Learning-Enabled Medical Devices Authorized by the US Food and Drug Administration From 1995 to 2025: Longitudinal Content Analysis.

BACKGROUND: The US Food and Drug Administration (FDA) has authorized AI-enabled and machine learning (ML)-enabled medical devices since 1995 and maint...

Understanding the Functional Needs of Patients With Cardiovascular Disease Regarding Nursing Robots Using a Kano Survey Scale: Cross-Sectional Study.

BACKGROUND: With population aging and the growing burden of chronic diseases, the number of patients with cardiovascular disease (CVD) in China contin...

ICU Open-Access Databases for Artificial Intelligence in Sepsis: Balancing Innovation With Data Quality Challenges.

OBJECTIVES: To evaluate the suitability of three major open-access ICU databases (Medical Information Mart for Intensive Care IV [MIMIC-IV], eICU Coll...

A Methodological Framework for Studying Online Language-Vision Interplay in Human-Robot Interaction.

Investigating the real-time interplay between language and vision has traditionally involved a trade-off between experimental control and interactive ...

Comment on: "A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review" (Marzouk et al., 2025).

Marzouk et al. reviewed 147 studies on artificial intelligence (AI) applications for predicting drug-drug, drug-disease, and drug-nutrient interaction...

MRI-Based Deep Learning Guides Multi-Omics Discovery of NBPF4 as a Therapeutic Target for Breast Cancer Lymph Node Metastasis.

Deep learning models are increasingly used to analyze medical images, but their "black box" nature makes it hard to understand the underlying biology ...

Advances in multimodal data integration for drug efficacy prediction: Methodological evolution and clinical translation.

Drug efficacy prediction remains a cornerstone of drug development and precision therapy. However, integrating heterogeneous biomedical data, includin...

Deployer-side governance of medical imaging artificial intelligence: the regulatory readiness instrument (RRI-MI) for multi-jurisdictional and post-market compliance.

Artificial intelligence (AI) is transforming medical imaging and digital health, yet standard pre-market clearances evaluate algorithms under static, ...