The recent developments in the field of large language models (LLMs) and their applications in healthcare have shown significant advancements, particularly in enhancing diagnostic accuracy, improving patient-doctor interactions, and optimizing medical data processing. The integration of multimodal data, such as combining text with medical images, has led to the creation of specialized models that can interpret complex medical data more effectively. These models, often fine-tuned with domain-specific data, are demonstrating capabilities beyond general-purpose LLMs, especially in non-English speaking regions where localized models are being developed. Additionally, there is a growing emphasis on making these models more accessible and efficient, with innovations in data pipeline optimization and alignment techniques that reduce computational costs and time. Notably, the deployment of AI in real-world medical settings is being rigorously tested for safety and efficacy, with promising results suggesting that AI can enhance patient experiences while maintaining high standards of care under physician supervision. The field is also witnessing a shift towards more transparent and open-source model development, with datasets being released to facilitate broader research and application of LLMs in healthcare. Overall, the direction of the field is towards more specialized, efficient, and accessible AI solutions that can be integrated into existing healthcare systems to improve patient outcomes and operational efficiency.