AI Medical Compendium

An exploratory study on the effectiveness of AI detection tools in identifying AI-generated articles.

International journal of oral and maxillofacial surgery Jan 31, 2026

Accurate identification of AI-generated content is critical for preserving scientific credibility. This exploratory study was performed to assess the effectiveness of eight AI detection tools (free versions) in differentiating human-written from AI-g... read more

Intelligent multi omics and industry 6.0 and 7.0 enabled technologies for deciphering the tumor immune microenvironment and advancing cancer immunotherapy.

Toxicology research Jan 31, 2026

Recent advancements in cancer immunotherapy have transformed clinical oncology, with monoclonal antibodies (mAbs), immune checkpoint inhibitors, adoptive cellular therapies, oncolytic viruses, cytokine based therapeutics and nanomedicine establishing... read more

Parent Experience During Pediatric Medical Visits With Virtual, In-Person, or No Scribes: A National Cross-Sectional Study.

Clinical pediatrics Jan 31, 2026

This study evaluated parent perceptions during outpatient pediatric medical visits with presence of medical scribes (virtual or in-person) or no scribe. A national, cross-sectional online survey was completed by 2148 parents of children 0 to 17 years... read more

Revisiting reliability with human and machine learning raters under scoring design and rater configuration in the many-facet Rasch model.

The British journal of mathematical and statistical psychology Jan 31, 2026

Constructed-response (CR) items are widely used to assess higher order skills but require human scoring, which introduces variability and is costly at scale. Machine learning (ML)-based scoring offers a scalable alternative, yet its psychometric cons... read more

Machine learning for predicting outcomes, complications and resource utilisation after hip arthroscopy: A systematic review.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA Jan 31, 2026

PURPOSE: Machine learning (ML) algorithms are increasingly used to predict outcomes in orthopaedic surgery, but their utility in hip arthroscopy remains unclear. This study aimed to (1) evaluate ML models for predicting outcomes, complications, and r... read more

ML Workflows for Screening Degradation-Relevant Properties of Forever Chemicals.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Jan 31, 2026

The environmental persistence of per- and polyfluoroalkyl substances (PFAS) necessitates new remediation technologies, yet the vast chemical space makes traditional exploration methods for understanding degradation-relevant properties intractable. Ra... read more

Accelerating the Exploration of Top Interface Passivators via Machine Learning for High-Performance Perovskite Solar Cells.

Small (Weinheim an der Bergstrasse, Germany) Jan 31, 2026

Interface passivation at the perovskite/electron-transport-layer (ETL) is key to reducing defects in perovskite solar cells (PSCs), yet the broad chemical space of passivators hinders discovery. Here, we present a machine-learning (ML)-guided workflo... read more

How AI Shapes the Future Landscape of Sustainable Building Design With Climate Change Challenges?

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Jan 31, 2026

Faced with climate change challenges, artificial intelligence (AI) is redefining the way of sustainable building design. In this work, how AI technologies, including foundation models and generative systems, are reshaping architectural practice in re... read more

Machine Learning Assisted Selective Configuration Interaction for Accurate Ground and Excited State Calculations.

Journal of chemical theory and computation Jan 31, 2026

In this work, we introduce a perturbative Selective Configuration Interaction (SCI) approach guided by a binary machine-learning classifier. The method leverages a lightweight feedforward neural network (FNN), purposefully designed for fast and effic... read more

Machine-Learning Framework for Excitation Energies of Chromophores in Polarizable Environments.

Journal of chemical information and modeling Jan 31, 2026

Excited states of embedded chromophores are highly influenced by their interaction with the environment. Herein, we present a machine-learning (ML) framework capable of predicting the different environmental contributions to excitation energies of ch... read more

Artificial Intelligence Medical Compendium