Artificial Intelligence Medical Compendium

PURPOSE: To evaluate the performance of ChatGPT on the National Prosthodontics Resident Exam (NPRE). METHODS: Two separate OpenAI accounts were used for ChatGPT 3.5 and ChatGPT 4.0, each managed by independent examiners. The dataset was sourced from ... read more 

BACKGROUND: Artificial intelligence (AI) applications for pediatric fracture diagnosis using radiographs have demonstrated growing potential in clinical settings. Despite this growing potential, existing studies are limited by small sample sizes, var... read more 

B-cell acute lymphoblastic leukemia (B-ALL) is an aggressive hematological malignancy that primarily affects children but can also occur in adults, progressing rapidly and requiring urgent clinical intervention. Late-stage diagnosis often results in ... read more 

Sepsis remains a major cause of mortality worldwide, driven by a dysregulated host response to infection that leads to life-threatening organ dysfunction. Despite advances in evidence-based medicine, early diagnosis and risk stratification remain sig... read more 

The genome sequences of organisms form a large and complex landscape, presenting a significant challenge in bioinformatics: how to utilize mathematical tools to describe and analyze this space effectively. The ability to compare relationships between... read more 

INTRODUCTION: Since the 2000s, artificial intelligence (AI) publications in medicine have surged, particularly in orthopaedics and radiology. A key area is the diagnosis of anterior cruciate ligament (ACL) tears, where AI enhances detection and treat... read more 

Artificial intelligence (AI) is rapidly transforming health care, with significant implications for neurosurgery. This essay provides a focused overview of contemporary and emerging AI models relevant to neurosurgery, with particular emphasis on natu... read more 

This study aimed to identify diagnostic marker genes for myocardial infarction (MI) and analyzed the key genes pertaining to immune cell infiltration. The MI expression microarrays GSE48060 and GSE66360 were retrieved and downloaded from the GEO data... read more 

OBJECTIVES: To develop and validate a deep learning model for automated detection of advanced liver fibrosis using standard T2-weighted MRI. METHODS: We utilized two datasets: the public CirrMRI600 + dataset (n = 374) containing T2-weighted MRI scans... read more 

BACKGROUND: Robust clinical trial data provide a key component for the development of evidence-informed medicine. However, clinical trial data may demonstrate treatment-effect heterogeneity, where some patients benefit from an intervention while othe... read more