AI Medical Compendium

SLPM: a lightweight deep learning model for end-to-end paper ECG digitization.

Physiological measurement Jan 30, 2026

OBJECTIVE: The digitization of paper electrocardiograms (ECGs) faces several challenges, including amplified errors during segmentation and signal extraction, severe noise interference, and poor generalization under complex conditions. To address the... read more

Integrating Medicinal Chemist Expertise with Deep Learning for Automated Molecular Optimization.

Journal of medicinal chemistry Jan 30, 2026

Successful compound optimization heavily relies on medicinal chemist expertise. In this work, we curated nearly 9000 molecular optimization strategies from the medicinal chemistry literature. Driven by expert knowledge, we constructed the MolOpt fram... read more

A novel endoscopic ultrasound system assisted by artificial intelligence for the recognition of pancreatic parenchyma and the detection of solid/cystic lesions.

Endoscopy Jan 30, 2026

ArtiDock: Accurate Machine Learning Approach to Protein-Ligand Docking Optimized for High-Throughput Virtual Screening.

Journal of chemical information and modeling Jan 30, 2026

Classical protein-ligand docking has been a cornerstone technique in computational drug discovery for decades but has reached an accuracy and performance plateau. Recently introduced Machine Learning (ML)-based docking methods offer a promising parad... read more

High-Throughput Physiologically Based Pharmacokinetic Model for Rodent Pharmacokinetics Prediction Using Machine Learning-Predicted Inputs and a Large In Vivo Pharmacokinetics Data Set.

Molecular pharmaceutics Jan 30, 2026

Accurate prediction of the pharmacokinetic (PK) properties of small-molecule drug candidates is a critical aspect of pharmaceutical research. Fast and reliable PK predictions can accelerate compound optimization cycles, reduce animal testing, and enh... read more

Machine Learning Algorithms to Predict Venous Thromboembolism in Patients With Sepsis in the Intensive Care Unit: Multicenter Retrospective Study.

JMIR medical informatics Jan 30, 2026

BACKGROUND: Venous thromboembolism (VTE) is a common and severe complication in intensive care unit (ICU) patients with sepsis. Conventional risk stratification tools lack sepsis-specific features and may inadequately capture complex, nonlinear inter... read more

Patient-reported outcomes as predictors of disability evolution in Multiple Sclerosis: An interpretable machine learning approach.

Multiple sclerosis (Houndmills, Basingstoke, England) Jan 30, 2026

BACKGROUND: Disability accrual in multiple sclerosis (MS) is highly variable and challenging to predict, complicating personalised care. Integrating machine learning (ML) with patient-reported outcomes (PROs) and clinician-assessed outcomes (CAOs) ma... read more

Update of the Novara Cohort Study (NCS): protocol evolution of a population-based longitudinal study on ageing in Northern Italy - cohort profile.

BMJ open Jan 30, 2026

PURPOSE: The Novara Cohort Study (NCS) was established to investigate the biological, psychological and social factors that influence ageing in the general population. The study aims to identify early risk factors for frailty, allostatic load and cog... read more

When I use a word . . . Artificial intelligence-predicting and detecting adverse drug reactions.

BMJ (Clinical research ed.) Jan 30, 2026

The 11th IUPS Workshop for Physiology Education: Towards a new global horizon in the teaching of physiology. Löwenstein, Germany, 14-16 Sept 2025.

Advances in physiology education Jan 30, 2026

Artificial Intelligence Medical Compendium