AI Medical Compendium

Validity of AI-generated multiple-choice questions in medical education: a systematic review.

Postgraduate medical journal May 20, 2026

Large language models (LLMs) are increasingly used to generate multiple-choice questions (MCQs) in medical education. We conducted a systematic review following PRISMA 2020, searching PubMed, Web of Science, Scopus, and ERIC through 15 February 2026.... read more

HD-MUNet: integrating artificial intelligence and high-density electromyography for motor unit number estimation.

IEEE transactions on bio-medical engineering May 20, 2026

OBJECTIVE: In this study we developed and evaluated the performance of HD-MUNet: a novel method for motor unit number estimation (MUNE), integrating high-density surface electromyograms (HD-sEMG) with artificial intelligence (AI). METHODS: We designe... read more

A Multi-Feature Fusion Framework for Automated Classification of Obstructive and Central Hypopneas in Polysomnography.

IEEE transactions on bio-medical engineering May 20, 2026

OBJECTIVE: This study aims to develop and validate a fully automated, interpretable machine learning system for the precise classification of obstructive (OH) and central (CH) hypopneas in polysomnography (PSG), directly addressing the critical issue... read more

Partial Contrastive Learning for Partially View-aligned Multi-view Clustering.

IEEE transactions on pattern analysis and machine intelligence May 20, 2026

Multi-view clustering has achieved promising performance with the advancement in deep neural networks. However, most existing multi-view clustering methods assume that cross-view correspondences are fully known, which is often unrealistic in real-wor... read more

Explainable Deep Learning Framework for Multimodal Brain Tumor Classification via Neuroimaging Attribute Extraction.

IEEE journal of biomedical and health informatics May 20, 2026

Accurate grading of brain tumors from multiparametric MRI is a critical step in treatment planning, yet deep learning models trained for this task remain opaque in their reasoning, limiting their acceptance in clinical settings that require transpare... read more

Unveiling Viral Escape Mechanisms With Machine Learning: A Transformative Approach to Mutation Analysis for SARS-CoV-2 and Beyond.

IEEE transactions on computational biology and bioinformatics May 20, 2026

Persistent viruses like Influenza, HIV, and Coronavirus exemplify the challenge of viral escape, significantly hindering the development of long-lasting vaccines and effective treatments. This study leverages a Long Short-Term Memory (LSTM) based dee... read more

Ontogenetic shifts in morphology and ecology of eastern Pacific white sharks revealed by computer vision.

PloS one May 20, 2026

Body size is a fundamental property of animal physiology, growth, and maturation, yet field measurements remain difficult to acquire for large-bodied, highly mobile marine species such as white sharks (Carcharodon carcharias). In this study, we integ... read more

Exploring Important Features in Continuous Spectral Datasets Using Supervised Learning.

Analytical chemistry May 20, 2026

Spectral characterization and analysis of materials often involve examining large, complex, multidimensional data sets that require both time and domain experience. Supervised learning methods are exceptionally useful for identifying the most relevan... read more

LSTM-attention-guided graph neural networks for integrated genotype-Environment modeling in maize yield prediction.

PLoS computational biology May 20, 2026

This paper presents a deep-learning framework that combines an LSTM, a graph neural network (GNN), and transformer-style attention to model genotype-environment (G×E) effects for maize yield prediction. Weather data for a growing season is summarized... read more

Toward transparent intelligence: Explainable stacked ensembles learning for LiDAR point cloud segmentation.