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healthcare-ai

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A Python-based computer vision and AI system for skin disease recognition and diagnosis. Led end-to-end project pipeline, including data gathering, preprocessing, and training models. Utilized Keras, TensorFlow, OpenCV, and other libraries for image processing and CNN models, showcasing expertise in deep learning and machine learning techniques.

  • Updated Apr 8, 2024
  • PureBasic

Official implementation of "UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis" - A unified medical vision-language model that integrates multimodal understanding and generation capabilities.

  • Updated Jan 15, 2026
  • Python

Healthcare AI Model Evaluator (HAIME) empowers healthcare organizations to independently evaluate and customize AI solutions, addressing challenges of transparency, clinical relevance, and real-world impact. By putting control in the hands of clinical professionals, it enables confident, context-specific adoption of AI in healthcare.

  • Updated Feb 17, 2026
  • TypeScript

MediScan: AI-powered bone fracture detection system achieving 99.8% accuracy through deep learning. Features real-time X-ray analysis, transparent Grad-CAM visualizations, and clinical integration tools. Built with Python/FastAPI backend and responsive HTML/CSS frontend, making advanced medical diagnostics more accessible to healthcare providers.

  • Updated Sep 7, 2025
  • Jupyter Notebook

An innovative application designed to help pharmacists and pharmacy students quickly research FDA-approved drugs by retrieving relevant information from drug labels and adverse event datasets, and providing AI-generated summaries to streamline the learning process

  • Updated Mar 24, 2025
  • Jupyter Notebook

Advanced multi-agent Medical AI Assistant powered by LangGraph that delivers empathetic, doctor-like responses using a hybrid pipeline of LLM reasoning, RAG from medical PDFs, and intelligent fallback tools. Features Long-term memory with SQLite, dynamic tool routing, and state reasoning for reliable, context-aware consultation.

  • Updated Feb 28, 2026
  • Jupyter Notebook

State-of-the-Art Pneumonia detection from chest X-rays system using EfficientNetV2 + FPN + Faster R-CNN. Features Focal Loss, Weighted Box Fusion, Mosaic Augmentation & StratifiedGroupKFold. Built for RSNA Pneumonia Detection Challenge. Achieves competitive mAP with mixed-precision training.

  • Updated Dec 3, 2025
  • Python

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