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Case Study
Explore how our innovative solutions have empowered businesses to overcome challenges and achieve remarkable success.
Explore how our innovative solutions have empowered businesses to overcome challenges and achieve remarkable success.
Implemented a LlamaIndex-based retrieval system to process clinical notes, patient histories, disease records, diagnostic reports, and other medical documents into vector embeddings. This created a semantic search layer across the hospital’s clinical knowledge base, enabling the system to identify relevant historical cases based on meaning, symptoms, and clinical context rather than exact keyword matches. The approach made decades of previously difficult-to-search medical information accessible to the diagnostic workflow.
Integrated a large language model with the retrieval layer to combine patient-provided symptoms with relevant hospital records. Retrieved clinical passages are supplied as context to the model before response generation, allowing it to produce more relevant and evidence-grounded diagnostic insights. The system can identify possible conditions, highlight relevant historical patterns, and present findings in natural language suitable for both clinical and patient-facing interactions.
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Used LangChain to connect symptom input, semantic retrieval, context assembly, prompt management, and LLM response generation into a unified real-time workflow. The orchestration layer manages the complete query lifecycle and ensures retrieved information is appropriately structured before being passed to the model. Conversation history is also maintained, enabling follow-up questions and progressively refining the assessment as additional patient information becomes available.
Integrated the complete RAG architecture into a mobile chat-based application, providing clinicians and patients with an accessible interface for symptom-based diagnostic support. Users can describe symptoms in natural language without needing to follow rigid medical forms, while the system retrieves relevant clinical information and returns contextual responses. The application also supports conversation sessions, history management, and secure processing of patient-related information for practical healthcare use.
