The recent developments in the research area demonstrate a significant shift towards leveraging advanced machine learning techniques, particularly Large Language Models (LLMs), to address complex, real-world problems across various domains. A notable trend is the integration of LLMs into systems designed to enhance personalization and interpretability, while also optimizing for efficiency and effectiveness. For instance, there is a growing focus on using LLMs for ingredient substitution in food recipes, which not only personalizes culinary experiences but also introduces creative possibilities. Similarly, LLMs are being employed to analyze social determinants of health in medical decision-making, particularly in liver transplantation, highlighting disparities and suggesting interventions for more equitable care. In the realm of food recommendation systems, researchers are developing multi-objective frameworks that balance user preferences with health considerations, enhanced by LLM-driven interpretability to educate users on healthy choices. Additionally, there is a move towards creating unbiased and interpretable AI frameworks, such as DSAI, which extract latent features from data in a manner that is both transparent and grounded in the actual data patterns. These advancements collectively push the boundaries of what AI can achieve, making it more accessible, understandable, and impactful in addressing societal challenges.