Artificial Intelligence Medical Compendium

Pesticide residues pose threats to the environment and food safety, and accurate and efficient detection technologies are key to addressing the challenges of pesticide residues. Theoretical calculations of Raman spectroscopy can guide molecular struc... read more 

OBJECTIVES: Romania is aligning its healthcare AI ecosystem with the European Union's AI Act (2024/1689), through its own National AI Strategy. Despite a growing volume of research in medical AI, significant gaps remain in translating this output int... read more 

Objective This study aimed to develop a clinical model in which the C-peptide index (CPI) under non-fasting conditions can predict future insulin therapy in patients with type 2 diabetes mellitus (T2DM). Methods This was a single-center retrospective... read more 

BACKGROUND: Positron Emission Tomography (PET) is a powerful diagnostic tool, but its availability, high cost and radiation burden limit its accessibility. Deep learning-based denoising offers a potential solution by enabling low-count PET scans, red... read more 

PURPOSE: Precise quantification of myocardial blood flow (MBF) and flow reserve (MFR) in 18F-flurpiridaz PET significantly relies on motion correction (MC). However, the manual frame-by-frame correction leads to significant inter-observer variability... read more 

BACKGROUND: The primary objective of the study was to evaluate and compare the effectiveness of four artificial intelligence (AI) platforms - ChatGPT, DeepSeek AI, Copilot, and Gemini - in providing solutions to common human resource (HR) challenges ... read more 

Myasthenia gravis (MG), a fluctuating autoimmune neuromuscular disease, presents unique challenges for remote assessment due to its reliance on detailed physical examination. To address this, we developed a quantitative telemedicine platform that aug... read more 

BACKGROUND: Endoscopic sinus surgery (ESS) fails to adequately address symptoms in some chronic rhinosinusitis (CRS) patients. This study aims to evaluate machine learning models (MLMs) in predicting ESS outcomes from preoperative data. METHODS: CRS ... read more 

Effective treatment of affective disorders (AD) requires a deep understanding of the underlying neurobiological mechanisms. However, in machine-learning-based analyses, cross-sectional studies have failed to identify robust individual-level biomarker... read more 

PURPOSE: Artificial intelligence (AI) emerged as a promising tool for enhancing healthcare delivery and outcomes for gastric cancer (GC) patients. This study aimed to analyze research development, patterns, and trends in AI applications on GC. METHOD... read more