The integration of artificial intelligence (AI) in healthcare has opened transformative avenues for enhancing patient care, decision-making, and accessibility to medical knowledge. This work presents a dual-function generative AI-based system that bridges the gap between complex clinical documentation and patient understanding. The system comprises two core modules: a Medical Report Analyzer that interprets unstructured and structured clinical data into patient-friendly insights, and a domain-specific Healthcare Chatbot trained on authoritative medical literature. By leveraging OpenAI’s large language models (LLMs) and semantic search with Pinecone vector embeddings, the system delivers accurate, context-aware responses and explanations. It simplifies clinical jargon, detects anomalies, and enables interactive health education. Built on a Flask-based architecture, this unified platform supports both real-time document analysis and conversational interactions. Evaluation results demonstrate strong performance in interpretability, relevance, and user engagement. This work lays a foundation for scalable, patient-centric AI solutions that enhance health literacy and empower informed decision-making.