Artificial Intelligence Medical Compendium

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

Showing 1,111 to 1,120 of 161,939 articles

When time is of the essence: ethical reconsideration of XAI in time-sensitive environments.

Journal of medical ethics
The objective of explainable artificial intelligence systems designed for clinical decision support (XAI-CDSS) is to enhance physicians' diagnostic performance, confidence and trust through the implementation of interpretable methods, thus providing ... read more 

T cell engagers: expanding horizons in oncology and beyond.

British journal of cancer
BACKGROUND/INTRODUCTION: T cell engagers (TCEs) are engineered immunotherapeutic molecules designed to direct the body's immune system against tumour or infected cells by bridging T cells and their targets, triggering potent cytotoxic responses. Over... read more 

Designing Pb-Free High-Entropy Relaxor Ferroelectrics with Machine Learning Assistance for High Energy Storage.

Journal of the American Chemical Society
High-entropy tactics present exceptional promise in advancing the dielectric energy storage of relaxor ferroelectrics, thereby benefiting various pulsed-power electronic systems. However, their vast composition space poses challenges in the rational ... read more 

Enhancing Quantum Federated Learning with Fisher Information-Based Optimization

arXiv
Federated Learning (FL) has become increasingly popular across different sectors, offering a way for clients to work together to train a global model without sharing sensitive data. It involves multiple rounds of communication between the global mo... read more 

Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions.

Biomedical engineering online
Understanding Cytochrome P450 (CYP) enzyme-mediated metabolism is critical for accurate Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictions, which play a pivotal role in drug discovery. Traditional approaches, while foun... read more 

Diagnostic technologies for neuroblastoma.

Lab on a chip
Neuroblastoma is an aggressive childhood cancer characterised by high relapse rates and heterogenicity. Current medical diagnostic methods involve an array of techniques, from blood tests to tumour biopsies. This process is associated with long-term ... read more 

Low-data machine learning models for predicting thermodynamic properties of solid-solid phase transformations in plastic crystals.

Soft matter
Plastic crystals, many of which are globular small molecules that exhibit transitions between rotationally ordered and rotationally disordered states, represent an important subclass of colossal barocaloric effect materials. The known set of plastic ... read more 

VisionTrap: Unanswerable Questions On Visual Data

arXiv
Visual Question Answering (VQA) has been a widely studied topic, with extensive research focusing on how VLMs respond to answerable questions based on real-world images. However, there has been limited exploration of how these models handle unanswe... read more 

Untangling the Postmortem Metabolome: A Machine Learning Approach for Accurate PMI Estimation.

Analytical chemistry
Accurate estimation of the postmortem interval (PMI) is crucial for medico-legal investigations, providing critical timelines for criminal cases. Current PMI methods, however, often lack precision, limiting their forensic utility. In this study, we d... read more 

Parametric Integration with Neural Integral Operators

arXiv
Real-time rendering imposes strict limitations on the sampling budget for light transport simulation, often resulting in noisy images. However, denoisers have demonstrated that it is possible to produce noise-free images through filtering. We enhan... read more