AI Medical Compendium

Magnetic susceptibility source separation (χ-separation) in quantitative susceptibility mapping.

Magnetic resonance imaging May 26, 2026

Conventional quantitative susceptibility mapping (QSM) outputs a voxel-averaged value, frequently obscuring co-localized paramagnetic iron and diamagnetic myelin through phase cancellation. χ-separation resolves this ambiguity by disentangling sub-vo... read more

Synthetic phase image modulated by two-dimensional sinusoidal profile based on magnitude image using for magnetic resonance imaging reconstruction.

Magnetic resonance imaging May 26, 2026

BACKGROUND: Magnetic resonance imaging (MRI) is a complex-valued technique incorporating magnitude and phase information, with phase images critical for susceptibility-weighted imaging and quantitative susceptibility mapping yet often absent in recon... read more

AI in the EP Lab - Mapping, Imaging, and Signal Interpretation.

Indian pacing and electrophysiology journal May 26, 2026

Artificial intelligence (AI) is the use of computational models to learn from electrical, anatomical and imaging data to assist or automate interpretation, prediction and decision making in arrhythmia diagnosis and treatment. This includes interpreta... read more

Assessing the trustworthiness of health guidelines recommendations: the Transparent, Rigorous, Usable, Standardised and Trustworthy Guide (TRUSTGUIDES) tools development.

Journal of clinical epidemiology May 26, 2026

BACKGROUND: Health guidelines play a central role in informing clinical practice, public health measures and health policy. But their trustworthiness may be undermined by factors such as insufficient methodological rigour, lack of transparency, confl... read more

Examining the impact of artificial intelligence on physicians' performance: mediating effects of innovative work behavior and skills enhancement.

Journal of health organization and management May 26, 2026

PURPOSE: While prior studies have explored artificial intelligence (AI) adoption in various sectors, the specific impact on employee performance in healthcare organizations remains underexamined, particularly regarding the mediating roles of innovati... read more

A dataset of 1.2 million molecules with DFT-level quantum chemical annotations for molecular representation learning.

Communications chemistry May 26, 2026

An informative molecular representation is prerequisite for the accurate prediction of molecular property by machine learning, but demands large-scale data enriched with detailed physicochemical information for its effective learning. Here, we introd... read more

CODE-II: a large-scale dataset for artificial intelligence in ECG analysis.

NPJ digital medicine May 26, 2026

Data-driven methods for electrocardiogram (ECG) interpretation are rapidly progressing. Large datasets have enabled advances in artificial intelligence (AI) based ECG analysis, yet limitations in annotation quality, size, and scope remain major chall... read more

Towards generalizable seizure monitoring: EpiVLM for cross-environment detection and classification.

NPJ digital medicine May 26, 2026

The translation of automated seizure detection from controlled clinical units to real-world settings is hindered by heterogeneous recording conditions and limited expert monitoring. We introduce EpiVLM, a multimodal vision-language system that combin... read more

Global evolution of robot-assisted cholecystectomy research in the era of artificial intelligence: a bibliometric and knowledge-mapping study.

Journal of robotic surgery May 26, 2026

OBJECTIVE: To systematically map the global evolution, collaborative networks, knowledge structure, and emerging research hotspots in robot-assisted cholecystectomy (RAC) using bibliometric and visualization techniques. METHODS: A comprehensive bibli... read more

Explainable machine learning-driven identification of heart failure biomarkers: a multi-model feature selection approach with SHAP-based interpretability.

Scientific reports May 26, 2026

Heart failure (HF) remains a major clinical challenge due to its complex pathophysiology and the limitations of existing biomarkers. In this study, we developed a robust machine learning (ML) framework to identify novel transcriptomic signatures of H... read more

Artificial Intelligence Medical Compendium