The integration of large language models (LLMs) is transforming various fields, including anomaly detection, mental health diagnosis, extended reality, combinatorial optimization, clinical decision support, radiology, and artificial intelligence. Recent developments have focused on leveraging multimodal LLMs and knowledge-infused frameworks to enhance inspection performance, provide actionable maintenance recommendations, and improve diagnostic accuracy. Notable advancements include the development of plug-and-play frameworks, multi-stage deliberative reasoning processes, and fine-grained reward mechanisms. In the field of mental health diagnosis, LLMs are being used to analyze clinical notes, social media posts, and other forms of text data to detect depressive symptoms and generate human-interpretable reasoning. The use of simulated patients and virtual simulation environments is also becoming increasingly popular. In extended reality, LLMs are being integrated to enhance user experience, enabling more realistic and engaging interactions. The field of combinatorial optimization is witnessing a significant shift towards collaborative problem-solving, with a focus on developing novel frameworks that enable multiple agents to work together to improve solving performance. In clinical decision support, LLMs are being used to enhance diagnostic accuracy and clinical reasoning ability, particularly in complex clinical scenarios. The integration of LLMs in radiology is improving the accuracy and reliability of radiology question answering systems. Overall, the integration of LLMs is pushing the boundaries of what is possible in various fields, enabling more accurate, helpful, and honest content generation, and paving the way for more immersive, interactive, and intelligent digital experiences.