diff --git a/README.md b/README.md index 68409630..fe83c2bc 100644 --- a/README.md +++ b/README.md @@ -62,19 +62,22 @@ https://github.com/Atharv714/nationalhackathon/assets/142321494/c13cde81-8ab2-49 Roadmap for NeuroWellness AI -Q1 2025 - Initial Development and Expansion +# Q1 2025 - Initial Development and Expansion AI-Powered Diagnostics: Enhance AI psychiatrist capabilities by integrating additional models for personalized mental health support. Facial Expression & Voice Recognition Integration: Improve emotion detection algorithms and refine the accuracy of facial and voice recognition. Blockchain Integration: Ensure that all data is encrypted and stored securely for privacy with blockchain-backed solutions. -Q2 2025 - Pilot and Community Engagement + +# Q2 2025 - Pilot and Community Engagement Launch Pilot Programs: Begin testing the platform with a select group of users to collect feedback and refine AI models. VHelp Community Launch: Expand the VHelp feature to facilitate users connecting with nearby mental health professionals. Health Token System: Implement the health token system, allowing users to earn rewards for helping others. -Q3 2025 - Expansion and Scaling + +# Q3 2025 - Expansion and Scaling Smartwatch Integration: Incorporate wearable tech into the platform to monitor real-time health metrics (heart rate, sleep patterns, etc.) and trigger emergency alerts. Large-Scale Deployment: Expand access to a broader user base, focusing on underserved regions for mental health support. AI Diagnosis Enhancements: Further improve AI-driven diagnosis features, offering more accurate prescriptions and emergency recommendations. -Q4 2025 - Long-Term Vision + +# Q4 2025 - Long-Term Vision Cross-National Partnerships: Collaborate with global organizations to increase the reach of NeuroWellness AI to international markets. Full Integration with Healthcare Providers: Build relationships with medical institutions for deeper integration of the platform into existing healthcare systems. Continuous AI Learning: Implement continuous learning techniques to ensure that the AI system adapts to new data and insights, improving over time.