Retrieval augmented generation for large language models in healthcare: A systematic review.

Large Language Models (LLMs) have demonstrated promising capabilities to solve complex tasks in critical sectors such as healthcare. However, LLMs are limited by their training data which is often outdated, the tendency to generate inaccurate ("hallucinated") content and a lack of transparency in the content they generate. To address these limitations, retrieval augmented generation (RAG) grounds the responses of LLMs by exposing them to external knowledge sources.

SDVSRM - a new SSRM based technique featuring dynamically adjusted, scanner synchronized sample voltages for measurement of actively operated devices.

Scanning spreading resistance microscopy (SSRM) with its high spatial resolution and high dynamic signal range is a powerful tool for two-dimensional characterization of semiconductor dopant areas. However, the application of the method is limited to devices in equilibrium condition, as the investigation of actively operated devices would imply potential differences within the device, whereas SSRM relies on a constant voltage difference between sample surface and probe tip. Furthermore, the standard preparation includes short circuiting of all device components, limiting applications to devices in equilibrium condition.

Essential Indicators Identifying Chronic Inorganic Mercury Intoxication: Pooled Analysis across Multiple Cross-Sectional Studies.

Background: The continuous exposure to inorganic mercury vapour in artisanal small-scale gold mining (ASGM) areas leads to chronic health problems. It is therefore essential to have a quick, but reliable risk assessing tool to diagnose chronic inorganic mercury intoxication. This study re-evaluates the state-of-the-art toolkit to diagnose chronic inorganic mercury intoxication by analysing data from multiple pooled cross-sectional studies.