The fields of recommender systems, large language models, and medical applications are undergoing significant transformations, driven by the need for more personalized, adaptive, and reliable approaches. A common thread among these areas is the pursuit of improved user experience, enhanced accuracy, and increased trustworthiness. In the realm of recommender systems, innovative techniques such as generative models, transfer learning, and reinforcement learning are being explored to address challenges like data sparsity and user addiction. Notable advancements include the development of tokenization techniques like MTGRec and UTGRec, which have shown promise in enhancing the representational capacity of recommender models. Furthermore, there is a growing emphasis on prioritizing user well-being, as seen in the SEC framework, which leverages imitation learning for user retention. Large language models (LLMs) are also evolving rapidly, with a focus on understanding their internal mechanisms and improving their capabilities. Researchers are investigating how multimodal knowledge evolves in LLMs, as well as methods to quantify uncertainty and detect potential vulnerabilities. The development of models like HyperLLM, which integrates large language models with hyperbolic space, has demonstrated potential in capturing hierarchical information and improving recommendation performance. In the medical domain, the application of large language models is gaining traction, particularly in generating medical reports, detecting errors, and providing explanations. The incorporation of complex reasoning and reflection mechanisms, as seen in the LVMed-R2 model, has enhanced the performance of these models in medical report generation. Additionally, there is a growing interest in developing explainable language models that can provide transparent and trustworthy explanations for their predictions. A significant challenge being addressed across these fields is the mitigation of hallucinations, which refers to the generation of non-factual or misleading content. Innovative approaches, including the use of linguistic nuances, bounded input perturbations, and noise augmented fine-tuning, are being explored to enhance the factual accuracy and robustness of large language models. These advancements collectively contribute to a future where AI systems are more personalized, reliable, and trustworthy, ultimately leading to improved user satisfaction and engagement across various applications.