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🚀 Issue: Explore AI-Enabled Diagnostics for Global Health

Description
Research and develop an AI-powered tool focused on early disease detection using sound, images, or external visuals. Inspired by real-world research detecting tuberculosis through cough sounds, anemia through eye photos, and skin conditions via image recognition, this project aims to democratize access to diagnostics for under-resourced areas.

Goals

• Design a prototype using AI models (e.g. vision or audio classification) to identify common diseases.
• Start with one use case (e.g., cough sound for TB, skin condition detection, or anemia via eye photos).
• Ensure compatibility with mobile/web platforms.
• Focus on privacy, accuracy, and low-resource usability.

Potential Stack

• Frontend: React or Angular
• Backend: Node.js / Python (FastAPI)
• ML: TensorFlow / PyTorch / ONNX models
• Data Source: Public medical datasets (e.g., HAM10000, TB cough sound datasets)
• Deployment: Firebase, Vercel, or Azure
• Optional: Integration with Google’s Teachable Machine for prototyping

Why It Matters
Over 1.6 billion people live with anemia, 2 million TB cases are reported in India alone annually, and millions go undiagnosed due to lack of access. This app could become a bridge between communities and healthcare services, enabling earlier detection and action. https://health.google/intl/en_id/health-research/imaging-and-diagnostics/

Labels: idea, healthtech, AI, ML, first-issue, needs-research