AI Medical Compendium

Curated and Structure-Based Drug-Target Interactions Improve Underprediction of Drug Side Effects in Network Models.

Journal of chemical information and modeling Jan 10, 2026

The accurate prediction of drug-induced side effects remains a significant challenge in pharmaceutical development, particularly in early development, as drug programs often fail due to unforeseen adverse reactions. Conventional approaches, such as p... read more

What matters most to older adults? A systematic review of preferences for socially assistive robots.

Archives of gerontology and geriatrics Jan 10, 2026

BACKGROUND: The current level of social acceptance of socially assistive robots (SARs) remains limited. Research on user preferences plays an essential role in improving the acceptance of SARs among older adults. This study aimed to integrate evidenc... read more

Automated extraction of fluoropyrimidine treatment and treatment-related toxicities from clinical notes using natural language processing.

International journal of medical informatics Jan 10, 2026

OBJECTIVE: Fluoropyrimidines are widely prescribed for colorectal and breast cancers, but are associated with toxicities such as hand-foot syndrome and cardiotoxicity. Since toxicity documentation is often embedded in clinical notes, we aimed to deve... read more

An old disease, a new linguistic challenge for large language models: patient education on psoriasis and psoriatic arthritis in an underrepresented medical language.

International journal of medical informatics Jan 10, 2026

OBJECTIVE: Large Language Models (LLMs) are increasingly applied to patient education, yet their performance in languages that are relatively underrepresented in medical-domain corpora and large language model training datasets remains underexplored.... read more

Multi-scale heart simulation augments the explainability of artificial intelligence-enabled electrocardiogram through provision of an electrocardiogram database labelled with cellular pathologies.

Computer methods and programs in biomedicine Jan 10, 2026

BACKGROUND AND OBJECTIVES: Although artificial-intelligence-enhanced electrocardiograms (AI-ECGs) offer prediction and diagnosis capabilities superior to those of humans, they exhibit poor explainability and interpretability because of their complex-... read more

Machine learning for the prediction of atrial fibrillation recurrence after catheter ablation: A systematic review and meta-analysis.

Computer methods and programs in biomedicine Jan 10, 2026

BACKGROUND AND OBJECTIVE: This systematic review evaluates the current state of Machine Learning (ML) methods for predicting Atrial Fibrillation (AF) recurrence following catheter ablation. With the growing use of ML, a systematic evaluation of perfo... read more

Coronary artery calcium clinical utilization: An update.

Current problems in cardiology Jan 10, 2026

Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide. Coronary artery calcification (CAC) is a well-established marker of atherosclerotic burden, and its quantification provides an objective measure of subclinica... read more

Development of a deep learning-based histological evaluation model for critical-size bone defect healing in rats - an objective tool.

Bone Jan 10, 2026

INTRODUCTION: Critical-size femoral defects in rats are a well-established model for preclinical bone regeneration research. Histological evaluation is essential for assessing healing but remains time-consuming and subject to observer variability. Ma... read more

Utilizing artificial intelligence for the diagnosis of ocular surface squamous neoplasia with ultrasound biomicroscopy images.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie Jan 10, 2026

PURPOSE: This study aims to develop an artificial intelligence (AI) model to assist ophthalmologists in distinguishing ocular surface squamous neoplasia (OSSN) from benign ocular surface lesions using ultrasound biomicroscopy (UBM) images. METHODS: D... read more

Machine Learning-Based Prediction Model for Delayed Chemotherapy-Induced Nausea and Vomiting in Pediatric Cancer: A Prospective Cohort Study.

Pediatric blood & cancer Jan 10, 2026

BACKGROUND: Delayed chemotherapy-induced nausea and vomiting (CINV) in pediatric oncology patients is currently under-recognized. This study aims to develop, validate, and visualize a machine learning-based model to predict delayed CINV risk in child... read more

Artificial Intelligence Medical Compendium