Transforming healthcare with ethical AI solutions that prioritize privacy, accessibility, and community collaboration.
Open-source tools that handle multilingual audio where speakers switch between languages mid-conversation—a common challenge in healthcare settings with diverse patient populations.
Community-built solutions that maintain patient confidentiality while enabling powerful AI analysis, designed specifically for healthcare's strict privacy requirements.
Collaborative tools that work effectively with limited training data, making AI accessible for underserved languages and communities in healthcare contexts.
Open frameworks that clean and process audio data from real-world healthcare environments—handling background noise, poor recording quality, and challenging acoustic conditions.
Production-ready, open-source AI models that bridge the gap between research and real clinical applications, with comprehensive documentation and community support.
Fine-tuned ASR model for Bengali-English speech translation. View on GitHub
A real-time AI-powered chat app using FastAPI, WebSocket, and Groq. View on GitHub
A RAG delivers comprehensive, context-aware insights on healthcare data privacy through a novel knowledge tree. View on GitHub
A fine-tuned Text-to-Speech (TTS) model trained on the MediBeng dataset, designed to handle bilingual Bengali-English code-switching in healthcare settings. View on GitHub
A hybrid AI model combining transfer learning (VGG19) and CNN for efficient sunflower disease detection, achieving superior precision, recall, and accuracy compared to other approaches. Read Paper
We present the MediBeng Whisper Tiny model, a cost-effective, fine-tuned solution for accurate transcription and translation of code-switched Bengali-English conversations in healthcare. Read Paper
This study analyzes machine learning techniques, with the Random Forest ensemble method achieving 90% accuracy in identifying fake news related to the COVID-19 pandemic. Read Paper
Contribute to our GitHub repositories, help improve existing tools, or build new solutions for healthcare AI challenges. All skill levels welcome—from bug fixes to major feature development.
Share your healthcare data challenges, test our tools in real-world scenarios, or collaborate on research papers. Your domain expertise helps make our solutions more practical and effective.
Help others by improving documentation, creating tutorials, answering questions, or sharing use cases. Building a supportive community is just as important as building great code.
We welcome mission-aligned partnerships, grants, and collaborations that advance ethical healthcare AI. We also offer implementation consulting to help organizations deploy our open-source tools effectively.
Have a healthcare data problem that needs solving? Want to suggest new features or research directions? Your insights from the field help guide our development priorities and real-world impact.