Heal Sphere: Health Recommendation System using LLM

This study will introduce a highly developed model of healthcare assistance through the combination of machine learning and multimodal Large Language Model (LLM) to improve the system of medical support and disease prediction. The new framework incorporates the existing clinical predictive methods with the Gemini 1.5 Flash multimodal LLM, which allows one to assess the medical situation both textually and visually. The system receives the symptoms, medical reports and diagnostic images of the patients and is capable of making accurate predictions as well as providing human type descriptions, treatment recommendations and even follow up instruction. The LLM is very vital in contextual interpretation, medical summarization and decision support improvement. This hybrid structure addresses drawbacks of individual ML models since it offers more in-depth semantic insight and flexibility to a variety of clinical settings. The research is in line with various United Nations Sustainable Development Goals (SDGs), especially those ones concerned with good health, innovation, minimized inequality, and stable communities. The performance analysis proves to be more accurate, easy to interpret and use than the current systems. The results determine the potential of using LLMs in predictive healthcare systems, which will allow the creation of safe, accessible, and cost-effective digital health solutions. The proposed system has a positive impact on the scalable AI-driven healthcare systems, particularly useful in the resource-constrained settings.