JeevanAi

Inspiration

Many people ignore early symptoms or rely on unreliable online searches, leading to delayed or incorrect treatment. One incident that truly highlighted this issue for me was when my father fell ill. Initially, he had common symptoms like fever and cold, so he bought over-the-counter medicines from a pharmacy without consulting a doctor. When his condition didn’t improve, he visited a doctor who prescribed basic medication, but there was still no recovery. Eventually, after further tests, he was diagnosed with dengue and had to be hospitalized.

This experience made me realize how critical timely and accurate medical assessment is—and how easily symptoms can be misinterpreted or overlooked. JeevanAI was born out of this need to bridge the gap between symptoms and appropriate medical care. It aims to help users receive early assessments, AI-driven insights, and relevant specialist recommendations—before it's too late.

What it does

JeevanAI collects user health history and symptoms through an AI-powered chat interface. It analyzes inputs in real-time, asks follow-up questions, and provides a medical assessment report with possible conditions, care recommendations, and suggest specialist doctor accordingly.

How we built it

We built JeevanAI using Next.js and React for a modern, responsive frontend, styled with Tailwind CSS for fast and clean UI development. The AI-powered symptom assessment leverages the Groq API with the LLaMA-3.1-8b model to interpret user inputs dynamically. We send symptom data and contextual prompts to the API and receive real-time, personalized responses.

For report generation, we used the jsPDF and jspdf-autotable libraries to create downloadable PDF medical assessment reports summarizing patient info, symptoms, assessments, recommendations, and specialist referrals.

The app integrates a specialist locator map using Leaflet with OpenStreetMap tiles. Users can enable location access (or manually set their location) to find and view nearby healthcare specialists relevant to their symptoms or illness. Specialist data is fetched dynamically from our backend API based on user queries and location.

Privacy is a priority: all sensitive data is processed in real time without persistent storage. This architecture ensures user data confidentiality while delivering a rich, interactive healthcare assistant experience.

Challenges we ran into

Designing an intuitive, natural conversation flow that balances medical accuracy with user simplicity was challenging. Integrating geolocation while maintaining privacy and ensuring minimal response latency required careful planning. Communicating the limits of AI-based health advice to users, emphasizing that this tool is not a replacement for professional medical diagnosis, was also important.

Accomplishments that we're proud of

Creating a fully interactive AI symptom checker that provides personalized assessments and specialist recommendations in real-time. Successfully integrating user health history and location-based clinics/ healthcare centres. Generating detailed PDF reports instantly after each assessment.

What we learned

Skills in building responsive and user-friendly web applications using Next.js, React, and Tailwind CSS were improved. Integration of AI APIs for medical symptom assessment and programmatic generation of detailed PDF reports was accomplished. Experience was gained in working with interactive maps and location services to provide personalized specialist recommendations. The role and limitations of AI in healthcare was recognized.

What's next for JeevanAI

We plan to expand symptom coverage and improve AI accuracy with larger datasets. Adding voice input and support for more languages is a priority to increase accessibility. We aim to partner with healthcare providers to integrate JeevanAI as a preliminary diagnostic tool and explore integration with electronic health records for seamless patient care.

Built With

• groq-ai-chat-completion-api
• jspdf
• leaflet.js
• next.js
• react
• tailwind-css
• typescript