Artificial Intelligence Medical Compendium

PURPOSE: To develop and evaluate a deep learning model that integrates ultra-widefield fundus photography and B-scan ultrasonography for automated classification of uveal melanoma (UM) and choroidal nevi. DESIGN: A retrospective cross-sectional study... read more 

CONTEXT: Bone fractures are among the most common musculoskeletal injuries and require timely, accurate diagnosis to ensure effective treatment and prevention of long-term complications. However, manual interpretation of X-ray images is often error-p... read more 

AIMS: Acute myocardial infarction (AMI) remains a leading global cause of mortality, where timely diagnosis is critical to enable early intervention. The 12-lead electrocardiogram (ECG) is a critical tool for AMI detection. While deep learning (DL) m... read more 

AIMS: Electrocardiograms (ECGs) and troponin (Tn) testing are essential tools for the diagnosis and management of cardiac conditions. Prompt diagnosis using these tools can significantly improve patient outcomes. METHODS AND RESULTS: The objective of... read more 

As an endocrine-disrupting chemical, 4-nonylphenol (4-NP) has been found above safe limits in global water systems and associated with cancer progression. This multidisciplinary study systematically investigated the carcinogenic mechanisms of 4-NP by... read more 

Accurate survival prediction in peritoneal dialysis (PD) patients is essential for personalized treatment planning and shared decision-making. We developed and validated PD-PREDICT, an XGBoost-based model to generate dynamic mortality risk estimates ... read more 

PURPOSE: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options and poorer overall survival than other subtypes. Neoadjuvant chemotherapy (NACT) is often used to reduce tumor size and improve sur... read more 

BackgroundReal-world data can inform health care decisions by allowing the evaluation of nuanced treatment strategies. Longitudinal observational data enable the assessment of dynamic treatment regimes (DTRs), strategies that adapt treatment over tim... read more 

While the integration of artificial intelligence in orthopedics is accelerating, the focus has largely been on powerful but resource-intensive Large Language Models (LLMs). This editorial argues for a strategic shift towards Small Language Models (SL... read more 

OBJECTIVE: This study evaluates the predictive performance of various machine learning (ML) algorithms for postpartum hemorrhage (PPH), peripartum hysterectomy, and severe coagulopathy using routinely collected pre-delivery clinical and biochemical d... read more