Symptomaton-AI

Inspiration

Lifestyle diseases are rising rapidly, yet awareness remains low. In India, lax regulation and widespread use of low-cost ingredients mean harmful exposures often go unnoticed. We saw a gap between diagnosis and true recovery—where simply removing triggers like chemicals, allergens, and pollutants could make a major difference.

What it does

Symptomaton-AI helps users identify hidden lifestyle and environmental triggers behind their symptoms. Instead of only suggesting treatments, it focuses on uncovering root causes to enable prevention-first healthcare.

How we built it

We built Symptomaton-AI using Gemini AI, combining symptom analysis with environmental and behavioral inputs. The system maps patterns between reported symptoms and likely exposures, using contextual insights from urban Indian settings like Delhi.

Challenges we ran into

The biggest challenge was linking vague, subjective symptoms to real-world exposures. We also dealt with noisy, inconsistent data and worked to ensure outputs are clear, explainable, and actionable for non-expert users.

Accomplishments that we're proud of

We created a functional AI system that shifts the narrative from reactive treatment to proactive prevention, tailored to real-world conditions in India.

What we learned

We learned that health is deeply influenced by everyday exposure. Building effective AI requires not just accuracy, but simplicity, transparency, and strong contextual relevance.

What's next for Symptomaton-AI

We plan to improve personalization, integrate real-time exposure data, expand datasets, and collaborate with healthcare ecosystems to scale preventive care solutions.

Built With

• google-gemini