Artificial Intelligence Medical Compendium

Large Language Models (LLMs) hold significant promise for electrocardiogram (ECG) analysis, yet challenges remain regarding transferability, time-scale information learning, and interpretability. Current methods suffer from model-specific ECG encoder... read more 

Precise localization of coronary arteries in Computed Tomography (CT) scans is critical from the perspective of medical assessment of various heart pathologies. Although manifold methods exist that offer high-quality segmentation of coronary arteries... read more 

Oncogenic RAS mutations, which are common in human tumors and occur in about 30 % of cancer cases, present significant challenges to effective cancer treatment. Among the KRAS family, the KRAS-G12D mutation is a promising target for treating differen... read more 

There is a gap in real-world clinical adoption of machine learning (ML) solutions due to the inherent uncertainty and variability in treatment outcomes. To bridge this gap, we present a novel approach to the problem of medical treatment selection usi... read more 

BACKGROUND: Delirium remains one of the most consequential complications among critically ill patients in ICUs, exerting profound effects on morbidity, mortality, and annual healthcare costs exceeding $81 billion. Communication barriers between sedat... read more 

INTRODUCTION: Until recently, the widespread use of genetic markers in prostate cancer (PCa) has been limited by the complexities and cost of genomic data analysis. Artificial intelligence (AI), due to its ability to process large volumes of unstruct... read more 

BACKGROUND: Clinical practice currently lacks objective and accurate screening tools for minimal hepatic encephalopathy (MHE). Therefore, we aimed to develop an MHE prediction model based on common risk factors. METHODS: A total of 514 and 191 cirrho... read more 

The rapid advancement of generative artificial intelligence (AI) has led an increasing number of Chinese patients to incorporate AI tools into their clinical encounters. While prior research has explored individual attitudes toward AI-mediated diagno... read more 

BACKGROUND CONTEXT: Manual measurement of cervical sagittal parameters is time-consuming and exhibits significant interobserver variability. Existing artificial intelligence models fail when C7 is obscured by shoulder anatomy. PURPOSE: To develop and... read more 

MOTIVATION: Identifying drug-target interactions (DTIs) is a critical step in both drug discovery and drug repurposing. Accurate in silico prediction of DTIs can substantially reduce development time and costs. Recent advances in sequence-based metho... read more