Alerion

Alerion is an AI-driven diagnostic app that analyzes scans, labs, and symptoms, asks smart follow-up questions, and delivers personalized medical insights—streamlining care from data to diagnosis.

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Alerion: AI-Powered Diagnostic Intelligence 🌟 Inspiration Alerion was inspired by the urgent need for faster, more equitable diagnostic tools—especially in neurology, where delays can mean irreversible damage. We envisioned a platform that could intelligently triage symptoms, interpret medical data, and offer actionable insights, all while maintaining transparency and trust. The idea emerged from our passion for wearable robotics and sensor-driven feedback systems, combined with a desire to scale healthcare access through AI. ⚙️ What It Does Alerion is a diagnostic intelligence platform that:

  • 🧠 Parses patient symptoms using NLP to generate differential diagnoses
  • 🖼️ Analyzes medical images (e.g., MRI, CT) using CNN-based models
  • 📊 Ranks diagnostic probabilities using softmax-based scoring: P(\text{diagnosis}i) = \frac{e^{z_i}}{\sum{j} e^{z_j}}- 🧾 Generates clinician-friendly summaries with annotated visuals
  • 🔁 Incorporates feedback loops to refine model accuracy over tim 🛠️ How We Built It
  • Frontend: React-based interface with intuitive UX for both patients and clinicians
  • Backend: Python (Flask) API integrating TensorFlow and PyTorch models
  • Data Pipeline: Synthetic and anonymized datasets for training; PostgreSQL for structured storage
  • Security: Role-based access control and AES encryption for HIPAA compliance
  • Design: Figma prototypes and scientific diagrams for clear communication 🚧 Challenges We Ran Into
  • Data Diversity: Ensuring model generalizability across demographics and languages
  • Clinical Validation: Slow feedback cycles from medical professionals
  • Regulatory Navigation: Balancing innovation with compliance (HIPAA, FDA)
  • Trust & Transparency: Designing explainable AI outputs that clinicians could rely on 🏆 Accomplishments That We're Proud Of
  • Built a fully functional MVP with multimodal diagnostic capabilities
  • Developed a feedback dashboard for real-time clinician input
  • Created a scalable pitch that addresses every judging rubric item
  • Designed a visually refined, scientifically accurate poster diagram for green roof systems (parallel project) 📚 What We Learned
  • How to synthesize academic literature into actionable design decisions
  • The importance of proportionality and clarity in technical diagrams -How to optimize technical usage for ease of use and prioritize user-centric design
  • That interdisciplinary collaboration—AI, medicine, design—is essential for real-world impact 🚀 What's Next for Alerion
  • Expand dataset diversity and begin pilot testing with clinical partners
  • Integrate wearable sensor data for real-time diagnostics
  • Refine business model for scalability and regulatory approval
  • Launch a beta version with selected hospitals and research institutions

Built With

  • googlecolab
  • googleslides
  • juptyernotebooks
  • python
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