Artificial Intelligence Medical Compendium

OBJECTIVES: Chemotherapy-induced cardiotoxicity is still a major clinical problem, usually appearing subclinically before structural or symptomatic cardiac dysfunction appears. Standard surveillance methods use imaging and biomarkers, which are time-... read more 

BACKGROUND: Effective pain management is a vital aspect of quality nursing care, requiring sound knowledge, assessment skills, clinical judgment, and patient-centered communication. With advances in healthcare technology, Artificial Intelligence (AI)... read more 

BACKGROUND: Current research on generative AI in medical education focuses on AI's performance or risks, such as unreliability. We argue these issues are not isolated flaws but are symptoms of systemic contradictions that emerge when a technology is ... read more 

PURPOSE: This study aims to compare several free large language models (LLMs), identify which provides the most effective feedback, and investigate whether LLM-generated feedback can improve the accuracy and standardization of imaging reports produce... read more 

OBJECTIVE: AI models are increasingly adopted in clinical practice, yet their generalizability outside controlled validation settings remains unclear. We aimed to evaluate the real-world performance of an FDA-cleared commercial pulmonary embolism (PE... read more 

OBJECTIVE: Estimating early lesion progression in ischemic stroke is essential for assessing thrombolytic treatment efficacy. While computed tomography perfusion (CTP) and diffusion-weighted imaging (DWI) are commonly used to determine irreversible t... read more 

The mutation-aware test prioritisation system in this paper uses Graph Neural Networks (GNNs) to combine static program structure, dynamic execution traces, and mutation coverage into a hybrid graph representation to enhance regression testing. The f... read more 

Knowledge Tracing (KT) is a deep learning task aims at tracing students' knowledge states and predicting their future performance. However, existing methods overlook the impact of dynamically changing comprehensive difficulty on students' knowledge s... read more 

Recent studies on multi-class classification have made significant progress. However, many approaches still suffer from unsatisfactory accuracy, high computational cost, and insufficient class-specific feature representation. To address these issues,... read more 

Hyperspectral image (HSI) classification is essential in remote sensing, leveraging rich spectral and spatial information. Convolutional neural networks (CNNs) excel at extracting local features, while transformers capture global semantic information... read more