How can neural networks be used to improve the efficiency of sorting algorithms?

Sorting algorithms are fundamental to computer science, each with varying efficiency, stability, and complexity characteristics. Classical algorithms like Quick Sort, Merge Sort, and Bubble Sort have been extensively optimized over the years. However, with the advent of machine learning and neural networks, there is potential for dynamic enhancements that adapt to specific input patterns or distributions. This question explores the possibilities of using neural networks to adaptively select, combine, or even create hybrid sorting techniques to optimize performance beyond traditional methods. Could a machine learning model dynamically predict and apply the most efficient sorting strategy for a given data set in real-time, potentially outperforming conventional algorithms in certain scenarios?