AI Medical Compendium

Clinician preferences for explainable AI in critical care: a comparative study of interpretable models and visualizations for intubation decision support.

International journal of medical informatics Jan 18, 2026

BACKGROUND: The complexity of many AI models hinders their clinical adoption because the clinicians using them do not regard them as transparent. This study addresses the lack of clinician-centered explainable AI (XAI) interfaces by designing and eva... read more

Regression augmentation with data-driven segmentation.

Neural networks : the official journal of the International Neural Network Society Jan 18, 2026

Imbalanced regression arises when the target distribution is skewed, causing models, especially neural networks, to focus on dense regions and struggle with underrepresented (minority) samples. Despite its relevance across many applications, few meth... read more

Clinical validation of a unified data-driven respiratory motion correction technique in 18F-FDG PET/CT imaging of upper abdominal lesions: a real-world study.

EJNMMI physics Jan 18, 2026

PURPOSE: Respiratory motion (RM)-related artifacts significantly impact image quality and diagnostic accuracy in PET/CT imaging. This study aimed to prospectively evaluate the clinical utility of the unified data-driven respiratory motion correction ... read more

Prediction of left ventricular systolic dysfunction in left bundle branch block using a fine-tuned ECG foundation model.

Scientific reports Jan 17, 2026

Left bundle branch block (LBBB) is an important electrocardiographic (ECG) finding strongly associated with left ventricular systolic dysfunction (LVSD), a condition linked to poor clinical outcomes. Although early LVSD detection is crucial, standard... read more

Deep learning-based automatic adenoid segmentation and a novel volume-based index for adenoid hypertrophy assessment.

BMC oral health Jan 17, 2026

BACKGROUND: Adenoid hypertrophy is a common cause of pediatric obstructive sleep apnea (OSA), which can impair cognitive development and affect craniofacial development. Given that adenoids typically regress with age and may respond to oral appliance... read more

Machine learning and SHAP values for predicting coronary artery disease risk in Xinjiang, China.

European journal of medical research Jan 17, 2026

BACKGROUND: Accurate individual risk assessment is crucial for guiding and improving the prevention of atherosclerotic cardiovascular disease (ASCVD). Existing prediction models are primarily derived from Western Caucasian and Chinese Han populations... read more

An explainable and transparent machine learning approach for predicting dental caries: a cross-national validation study.

BMC oral health Jan 17, 2026

BACKGROUND: There has been a notable increase in artificial intelligence (AI) studies in dentistry. However, the inadequate use of proper validation methods has led to overly optimistic performance metrics of machine learning (ML) models. External va... read more

Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.

Journal of nanobiotechnology Jan 17, 2026

Extracellular vesicles (EVs) are emerging as naturally bioactive nanomaterials with intrinsic biocompatibility and targeting potential. Recent integration of machine learning (ML) into EV research has accelerated advances in molecular profiling, stru... read more

Reference Accuracy in Large Language Model Chatbots: A Metric for Inherent Misinformation?

Medical science monitor : international medical journal of experimental and clinical research Jan 17, 2026

BACKGROUND We suggest that testing a large language model (LLM) chatbot in terms of the accuracy of the references it provides could be a powerful, quantifiable means of rating its inherent degree of misinformation, since the accuracy of the bibliogr... read more

Humans Artificial Intelligence Language Communication Generative Artificial Intelligence Large Language Models

Recognition of Normal Fetal Echocardiograms Based on an Explainable Denoising Deep Learning Model.

Journal of clinical ultrasound : JCU Jan 17, 2026

PURPOSE: To evaluate the proposed explainable denoising deep learning model, Grouped Shared Convolutional Attention Vision Transformer (GSCAViT), for classifying normal fetal echocardiogram. METHODS: A retrospective study was conducted on 358 fetal c... read more

Artificial Intelligence Medical Compendium