The recent advancements in the field of healthcare technology have seen a significant shift towards leveraging large language models (LLMs) and specialized multi-agent frameworks to enhance clinical capabilities and reduce clinician burden. The integration of LLMs into mobile devices, such as MedMobile, has demonstrated the potential for expert-level clinical reasoning at reduced computational costs, addressing privacy concerns while maintaining high performance on medical benchmarks. Additionally, the implementation of Electronic Medical Records (EMR) systems in various healthcare facilities has highlighted the benefits of real-time data access and improved resource management, leading to reduced healthcare costs and enhanced patient satisfaction. Noteworthy is the development of MedAide, an LLM-based multi-agent collaboration framework, which excels in personalized medical recommendations and diagnosis analysis, outperforming current LLMs in specialized healthcare services. Furthermore, the automation of progress note generation using structured hospital data has shown promise in alleviating clinician workload, with models like Biomistral achieving high accuracy in leveraging relevant data. Optimized biomedical question-answering services, such as those integrating LLMs with Multi-BERT configurations, are also making significant strides in supporting healthcare professionals with reliable and responsive tools for managing complex information. The potential of LLMs in medical OSCE assessment is another area of innovation, with models like GPT-4 demonstrating strong alignment with human graders, suggesting a future where LLM-based grading could augment traditional methods. LLMD, a model designed for interpreting longitudinal medical records, showcases the importance of nuanced connections among patient records for accurate health analysis. IMAS, an agentic approach to rural healthcare delivery, underscores the need for context-sensitive, adaptive, and reliable medical assistance in underserved areas. Representation learning of structured data for medical foundation models, such as UniStruct, addresses the limitations of current tokenization methods in processing medical codes, enhancing the model's ability to handle complex structured data. MedINST, a meta dataset of biomedical instructions, tackles the scarcity of diverse and well-annotated datasets, while MeNTi bridges medical calculators and LLM agents with nested tool calling, improving LLM performance in intricate medical scenarios. Overall, these developments indicate a trend towards more personalized, efficient, and accessible healthcare solutions through advanced AI and machine learning techniques.