Inspiration- Healthcare is overwhelming. Patients often book appointments without fully understanding their symptoms, while hospitals face long wait times and overloaded systems. We saw a gap in clarity, not care.

What it does-

We built an AI-powered pre-consultation assistant that:

  • Collects structured symptom input
  • Categorizes urgency levels
  • Provides clear, educational guidance
  • Generates a concise intake summary for providers

The system does not diagnose, it supports smarter decision-making before a consultation.

How we built it- We designed a guided AI interface that-

1) Asks patients structured questions about symptoms 2) Categorises urgency 3) Provides educational explanations 4) Generates a concise intake summary Instead of replacing medical professionals, the system supports them by improving intake clarity and reducing repetitive consultation time.

Challenges we ran into

One of our biggest challenges was balancing innovation with responsibility.
Healthcare is sensitive, so we had to ensure our system provides guidance without crossing into medical diagnosis.

Another challenge was scope. Healthcare is vast, and narrowing our focus to pre-consultation support required disciplined decision-making.

Accomplishments that we're proud of

  • Designed a clear AI-assisted intake workflow
  • Built a structured symptom-to-guidance system
  • Prioritized safety and explainability in our design
  • Created a solution that improves clarity without replacing doctors

We successfully transformed a complex healthcare problem into a focused, practical solution.

What we learned

We learned that AI in healthcare must support, not replace, human judgment.

We also learned that clarity reduces anxiety. When patients better understand their situation, consultations become more efficient and meaningful.

Responsible design matters as much as technical capability.

What's next for Diagnos.ai

  • Expanding into multilingual support
  • Integrating with scheduling systems
  • Incorporating real-time wait time predictions
  • Improving risk categorization using structured data

Our long-term goal is to create smarter, more efficient healthcare intake systems that scale responsibly.

Built With

  • gemini3flash
  • html
  • lovable
  • shadcn.ui
  • supabase
  • tailwindcss
  • typescript
  • vite(react)
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