X-AI Care

Open, Explainable Diagnostic Support System

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X-AI Care

Inspiration

  • Growing up in a small town in Himachal Pradesh, I saw how limited healthcare access can be.
  • Doctors only visited once or twice a week, and a close friend’s fracture went undiagnosed for nearly two weeks because no doctor was available.
  • This experience inspired me to build X-AI Care — not as a replacement for doctors, but as an assistive, explainable AI prototype that can:
    • Highlight concerns
    • Provide transparent insights
    • Improve communication between doctors and patients

What It Does

X-AI Care is an open, explainable diagnostic support system. It allows users to upload medical images (like X-rays), runs them through ML models, and generates:

  • Visual explainability (Grad-CAM heatmaps)
  • Feature attribution (SHAP values)
  • Counterfactuals (what-if scenarios)
  • Confidence scoring for predictions
  • Doctor-friendly reports with technical insights
  • Patient-friendly summaries simplified for understanding
  • Interactive chatbot powered by GPT OSS for case discussion
  • Dashboard and database to manage cases, store reports, and track analytics

How We Built It

  • Trained prototype ML models for wrist fracture
  • Integrated Grad-CAM, SHAP, and counterfactuals for explainability
  • Used confidence scoring to show prediction reliability
  • Implemented GPT OSS for automated report generation and chatbot interaction
  • Built a database and dashboard for storing cases, visualizations, and reports
  • Designed a dual report system:
    • Doctor report: detailed, technical insights
    • Patient report: simplified, empathetic summaries

Challenges We Ran Into

  • Responsible framing: ensuring this is clearly an assistive tool and not a diagnostic substitute
  • Integration complexity: combining ML models, explainability tools, GPT OSS, and dashboards into one cohesive system
  • Balancing outputs: making technical details digestible while keeping patient summaries simple
  • Resource limitations: simulating a scalable healthcare prototype within hackathon constraints

Accomplishments That We’re Proud Of

  • Created a full end-to-end pipeline: from image upload → explainability → GPT OSS reports → dashboard
  • Combined Grad-CAM, SHAP, and counterfactuals for multi-layer explainability
  • Built doctor and patient dual reports to bridge communication gaps
  • Designed a dashboard and database system that makes the project feel like a real prototype, not just a demo
  • Translated a real-life personal problem into a tangible solution

What We Learned

  • How to integrate multiple explainability techniques into medical AI
  • How GPT OSS can move beyond chat to power dynamic reporting and dialogue
  • The value of human-centered AI design in sensitive domains like healthcare
  • The importance of traceability and reproducibility in AI outputs through database storage

What’s Next for X-AI Care

  • Expanding to more medical models beyond fractures and cataracts
  • Adding bias and fairness checks to ensure reliability across diverse populations
  • Improving the dashboard with advanced analytics (model performance trends, case clustering)
  • Exploring integration with telemedicine platforms as an assistive layer
  • Refining reports further with multi-language support, so patients in rural areas can get insights in their local language

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