Selection Process

Selection processes, crucial across diverse fields from academic literature reviews to patient prioritization in emergency departments, aim to efficiently identify the most suitable candidates from a large pool. Current research emphasizes the development and application of artificial intelligence, particularly machine learning models like artificial neural networks and transformer-based architectures, to automate and optimize these processes. These advancements offer significant potential for improving efficiency, reducing bias, and enhancing the accuracy of selection decisions in various scientific and practical contexts, ranging from streamlining literature reviews to improving healthcare resource allocation. Furthermore, research is exploring methods to ensure fairness and equal opportunity within automated selection systems.