Artificial Intelligence Medical Compendium

Soft tissue sarcomas (STS) are heterogeneous malignancies with high recurrence rates (33-39%) post-surgery, necessitating improved prognostic tools. This study proposes a fusion model integrating deep transfer learning and radiomics from MRI to pred... read more 

Foundation models (FM) offer a promising alternative to supervised deep learning (DL) by enabling greater flexibility and generalizability without relying on large, labeled datasets. This study investigates the performance of supervised DL models and... read more 

High-resolution magnetic resonance spectroscopic imaging (MRSI) plays a crucial role in characterizing tumor metabolism and guiding clinical decisions for glioma patients. However, due to inherently low metabolite concentrations and signal-to-noise r... read more 

Protein-protein interactions (PPIs) are essential therapeutic targets, yet their large and relatively flat interfaces hinder the development of small-molecule inhibitors. Traditional computational approaches rely heavily on existing chemical librarie... read more 

PURPOSE: This study aimed to evaluate and compare the diagnostic performance of various Thyroid Imaging Reporting and Data Systems (TIRADS), with a particular focus on the artificial intelligence-based TIRADS (AI-TIRADS), in characterizing thyroid no... read more 

OBJECTIVE: This study explored the feasibility of preoperatively predicting perineural invasion (PNI) of intrahepatic cholangiocarcinoma (ICC) through machine learning based on clinical and CT image features, which may help in individualized clinical... read more 

Artificial intelligence (AI), particularly machine learning (ML), is increasingly influencing pharmacovigilance (PV) by improving case triage and signal detection. Several studies have reported encouraging performance, with high F1 scores and alignme... read more 

The German healthcare system is facing challenges in diagnosing coronary artery disease (CAD). These include high mortality rates, even when advanced medical technology is used, and an excessive number of coronary angiographies. A key issue is that c... read more 

AIMS: To develop and validate a machine learning-based risk prediction model for delirium in older inpatients. DESIGN: A prospective cohort study. METHODS: A prospective cohort study was conducted. Eighteen clinical features were prospectively collec... read more 

AIM: To examine the evolution of intensive care nurses' roles in pharmacological haemodynamic management from 1975 to 2025 and to explore projected responsibilities through 2075. DESIGN: A scholarly commentary. METHODS: A critical synthesis of litera... read more