The exponential growth of artificial intelligence (AI) in healthcare has paved the way for innovative solutions that enhance medical support and accessibility. This presents an Enhancing Digital Healthcare with AI: An Open Router-Based Medical Query System that empowers users with immediate medical insights through a conversational interface. The system integrates symptom-based diagnosis, drug information retrieval, and diseaserelated data. Built upon the OpenRouter API and FastAPI backend, the system leverages advanced natural language processing (NLP) models to interpret user queries accurately and generate medically relevant responses. Users can input symptoms, disease names, or drug names to receive detailed outputs, including probable diagnoses, medication usage, side effects, dosage, and dietary recommendations. The frontend, developed in React.js, ensures a userfriendly and responsive experience, making the system accessible to users with minimal technical knowledge. The project supports four primary use cases: symptom-based diagnosis, drug information retrieval, disease insights. Each use case is interconnected to allow seamless information flow—for instance, a diagnosis output includes relevant drug and dietary suggestions automatically. This interconnected architecture greatly enhances usability and coherence across modules. Testing of the system revealed high accuracy and speed in handling varied and complex user inputs. The AI models demonstrated effective contextual understanding and provided insightful, reliable responses in real-time. The integration of containerized deployment through Docker ensures cross-platform compatibility, robustness, and scalability of the application. This research underscores the potential of AI to assist in early-stage medical assessments and decision-making. While the system does not substitute professional healthcare services, it serves as an efficient preliminary assistant. Future work includes extending multi-lingual support, enhancing NLP capabilities, and incorporating speechbased interaction for broader accessibility.
Rajesh et al. (Wed,) studied this question.