Artificial Intelligence Medical Compendium

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The attention of office-based physicians is increasingly structured less by the patients in front of them than by the screen beside them - by the clicks, notifications, system lags, and task-switching that the practice information system demands. Res... read more 

PURPOSE: Emerging diagnostic, computational, and precision approaches are expanding the ability to detect, classify, and biologically characterise sepsis, but their translation into clinical benefit depends on whether added information changes manage... read more 

BACKGROUND: Gastric cancer (GC) remains a major global health burden, and early detection is essential for improving patient outcomes. Although GC screening has been widely implemented in East Asian countries with a high disease burden, the optimal s... read more 

The early detection of Parkinson's disease (PD) is critical for timely and effective clinical intervention and disease management. High dimensional gene expression data redundancy features, class imbalance and limited interpretability are challenges ... read more 

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Physicians are adopting generative AI tools faster than health systems can evaluate them. This commentary on the empirical report of "Shadow AI" drivers by Petersson and colleagues argues that governing Shadow AI requires health systems to pair insti... read more 

Artificial Intelligence (AI) language models are increasingly used in higher education. Many students use them for learning, often without proper academic policies or guidelines. Nursing undergraduate curricula include both theoretical and practical ... read more 

BACKGROUND: Occlusion myocardial infarction (OMI) on the electrocardiogram (ECG) can present as ST-elevation myocardial infarction (STEMI), non-STEMI, ST depression, or other patterns (eg, de Winter T waves and hyperacute T waves), posing a diagnosti... read more