AI driven diabetes reversal

AI-powered app to reverse Type 2 Diabetes with personalized diet plans, glucose tracking, and monthly expert consultations — tailored to your lifestyle.

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Inspiration

Diabetes is a growing health crisis, especially in India, where over 100 million people are affected. While medication can manage it, true reversal often lies in lifestyle changes—yet no platform offers personalized, adaptive, and expert-guided dietary solutions to support this. We identified a clear gap: patients require continuous support, personalisation, and motivation, not just general advice. This inspired us to build REVBERD—a digital therapeutic solution powered by AI and doctors.

What it does

REVBERD is a mobile app that helps users reverse Type 2 Diabetes naturally by:

  1. Collecting and analyzing past medical records
  2. Generating AI-powered personalized dietary plans
  3. Updating plans based on glucose readings every 3–7 days
  4. Integrating one-on-one monthly diabetic specialist consultations
  5. Optimizing diet plans based on the user's daily schedule and meal timings
  6. Allowing doctors to monitor and validate patient inputs

How I built it

Frontend: An attractive dashboard Backend: Superbase for real-time updates and secure user data Database: Superbase for patient records and history Doctor Dashboard: Web interface for specialists to review and update patient input Glucose Tracker Integration (in progress): Working to connect with wearable CGM APIs like FreeStyle Libre

Challenges I ran into

Data diversity: Medical records vary widely between patients; standardizing them was complex. AI accuracy: Generating useful, medically sound diet plans from varied inputs took time and iteration. User trust: Getting users to rely on AI while still involving real doctors was a balancing act. Compliance & Security: Ensuring HIPAA/GDPR-level protection for health data was a non-trivial challenge.

Accomplishments that I'm proud of

Created a working prototype that successfully adjusts meal plans based on glucose inputs. Built a realistic simulation of doctor-patient feedback loop in the app. Developed a custom AI diet engine with clinical data backing. Received positive feedback from early testers and medical professionals.

What I learned

How to build healthcare-grade tech that is both personal and scalable The value of continuous glucose monitoring and AI-driven adaptation Importance of trust, empathy, and usability in health-focused apps

What's next for Untitled

Conduct clinical pilot testing with 50+ patients Onboard more certified diabetic specialists Integrate wearable glucose trackers (CGMs) Launch a Beta version on Google Play and App Store Explore partnerships with hospitals, dietitians, and pharma companies Secure funding for scaling and real-world deployment

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