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NIX developed a clinical AI assistant on AWS Bedrock, reducing workload in Indian healthcare facilities by up to 40% and achieving 92% accuracy.
Healthcare
Chatbot, Data Science, AI, Prompt Engineering
AWS Bedrock, LangChain, Prompt Engineering, Python
Our client is an American startup dedicated to providing healthcare facilities in India with AI-powered services. To alleviate the overwhelming workload that physicians face daily, the company envisioned a sophisticated LLM-based clinical AI assistant. This clinical assistant is designed to assist physicians during patient examinations by evaluating health data and symptoms in real-time, delivering diagnostic suggestions and clinical insights. The client’s ultimate goal was to optimize healthcare workload and improve the overall state of healthcare services in India.
Following strategic interest and investment from AWS, the company sought to develop a robust proof of concept to validate the solution’s efficacy. They engaged NIX to leverage our deep expertise in GenAI technologies and prompt engineering, as well as our capabilities as an official AWS Advanced-tier Partner, ensuring the solution was built on a foundation of technical excellence and cloud-native best practices.
Recognizing the defined scope of the AI-powered clinical assistant PoC, our team decided to maximize its impact by developing a simple yet effective solution based on a lightweight back end and a single-endpoint API. For the core LLM provider, we chose AWS Bedrock for its seamless ecosystem integration and enterprise-grade security. Rather than conducting a full-scale model training—which was outside the project’s initial scope—we implemented a specialized system prompt tailored specifically for clinical environments.
Since we utilized AWS foundation to ensure compatibility and efficiency across all its infrastructure, our solution utilizes an event-driven AWS architecture, ensuring rapid response times. This architecture consisted of AWS Lambda for request processing, AWS Bedrock as a LLM provider, and Amazon S3 for secure storage.
As a result, the developed PoC presented a rigorous, logical flow to ensure clinical relevance:
The physician inputs patient symptoms and observations into the model.
The LLM analyzes the input in conjunction with the entire chat history to maintain continuity and clinical context.
Using the contextualized data, the Bedrock model generates diagnostic suggestions and health evaluations.
The refined output is delivered back to the user through the API.
Our engagement in the project successfully validated the client’s vision, providing a powerful tool to secure further investment for full-scale product development. Developed PoC serves as the definitive blueprint for a full-fledged healthcare solution, proving that AI can effectively bridge the gap in medical resource accessibility. Despite the initial limited scope, the PoC demonstrated impressive results in clinical accuracy and operational efficiency:
92%
diagnostic accuracy
40%
workload reduction
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