Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 8,711 to 8,720 of 208,216 articles

A macrophage-related efferocytosis-based two-gene prognostic model for acute myeloid leukemia identified by multi-omics and machine learning.

Annals of hematology
Background Acute myeloid leukemia (AML) remains a lethal hematologic malignancy with high heterogeneity. Macrophage-mediated efferocytosis in the tumor microenvironment is implicated in immune suppression and disease progression. Methods We integrate... read more 

Machine Learning-Based Prediction Model for 30-Day Emergency Department Revisits in a Medically Underserved Tertiary Hospital: Formative Retrospective Cohort Study.

JMIR formative research
BACKGROUND: Emergency department (ED) revisits are critical quality indicators, particularly in medically underserved areas, where traditional prediction tools show limited performance. Machine learning (ML) approaches may offer improved predictive p... read more 

Evaluating cognitive biases in AI-assisted mammography interpretation: a simulation reader study of explainable AI across radiologist experience levels.

European radiology
OBJECTIVES: To evaluate the impact of automation and anchoring bias in artificial intelligence (AI)-assisted mammography interpretation and to assess whether saliency-based explainable AI (XAI) mitigates these biases across radiologists of varying ex... read more 

Does Patient History Influence Capsular Contracture? An Exploratory Analysis with Machine Learning.

Aesthetic plastic surgery
BACKGROUND: Capsular contracture (CC) is a frequent and distressing complication of breast augmentation and reconstruction. Although numerous patient-, surgical-, and implant-related risk factors have been proposed, reliable population-level predicto... read more 

Harnessing and Suppressing Electron Spin-State Transitions: From Decoding to Rational Design of High-Performance Cathodes for Alkali-Ion Batteries.

Small (Weinheim an der Bergstrasse, Germany)
The electron spin state of transition metal ions represents a fundamental quantum property that is increasingly recognized as a pivotal design dimension for tuning the performance of cathode materials in Li/Na/K‑ion batteries. This review begins by c... read more 

Artificial Intelligence for Sleep Instability and Motor Phenotyping: Clinical Translation Beyond Sleep Staging.

Sleep
Sleep medicine has rapidly adopted artificial intelligence, but most applications still prioritize automated sleep staging or single summary indices, limiting clinical translation when symptoms arise from within-stage dynamics. This review proposes a... read more 

How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Drug molecular interactions, including drug-drug interactions (DDIs) and drug-target interactions (DTIs), are critical for drug discovery and clinical safety, increasingly propelled by artificial intelligence (AI) technologies. Although previously tr... read more 

Machine Learning Accelerated Non-Adiabatic Molecular Dynamics Elucidates Local Polarization Effects on Non-radiative Recombination in Halide Perovskites.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Non-radiative recombination is a critical factor limiting the optoelectronic performance of halide perovskites, yet how local polarization induced by charge redistribution regulates this process remains unclear. To gain deeper insight while reducing ... read more 

AI-microbial hybrid biosensors: the next generation of intelligent detection systems.

Future microbiology
The convergence of artificial intelligence (AI) and microbial biosensor technology is transforming pathogen detection, environmental surveillance, antimicrobial resistance (AMR) profiling, and precision diagnostics. Microbial biosensors exploit the s... read more 

Developing Predictive Models by Sharing Predictions - An Investigation of a Federated Learning Approach for ADMET Predictions.

Journal of medicinal chemistry
Machine learning models for ADMET prediction benefit from large, diverse data sets, yet such data are typically siloed across organizations. Federated learning (FL) enables collaborative modeling while preserving data privacy. Here, we investigate a ... read more