MediVerse

AI-powered pre-surgical assistant. Streamline notes, analyze endoscopies, and generate reports—all in one app. Empowering doctors with cutting-edge tech for better surgical prep and patient outcomes.

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Inspiration

The inspiration for MediVerse came from observing the time-consuming and often fragmented pre-surgical process. We saw an opportunity to leverage cutting-edge AI and cloud technologies to streamline this critical phase of patient care, potentially improving outcomes and reducing physician burnout.

What it does

MediVerse is an AI-powered pre-surgical assistant that:

  1. Captures pre-op notes through voice or text input in multiple languages
  2. Analyzes endoscopy videos using computer vision to identify anomalies
  3. Transcribes doctor's spoken observations during procedures
  4. Compiles all data into a comprehensive report using Google Cloud's BigQuery
  5. Provides an intuitive workflow from initial patient data to final report generation

How we built it

We built MediVerse using:

  • Next.js for the web application frontend
  • NLX for natural language processing and workflow management
  • Google Cloud for storage and BigQuery for data analysis
  • OpenCV for computer vision analysis of endoscopy videos
  • Google's Speech-to-Text API for voice input
  • Express.js for handling audio file uploads

Challenges we ran into

  1. Integrating multiple AI services (NLX, OpenCV, Speech-to-Text) seamlessly
  2. Ensuring real-time processing and analysis of endoscopy video feeds
  3. Maintaining data privacy and security while leveraging cloud services
  4. Optimizing the user interface for busy medical professionals
  5. Handling multi-language support for voice inputs and transcriptions

Accomplishments that we're proud of

  1. Creating a unified platform that streamlines the entire pre-surgical process
  2. Successfully integrating computer vision for real-time endoscopy analysis
  3. Implementing a multi-language voice input system for global usability
  4. Developing an intuitive UI that requires minimal training for medical staff
  5. Leveraging cloud technologies to enable scalable and efficient data processing

What we learned

  1. The complexities of healthcare workflows and the importance of user-centric design
  2. Advanced integration techniques for multiple AI and cloud services
  3. The challenges and solutions for handling sensitive medical data
  4. The potential of AI to significantly improve healthcare processes
  5. The importance of cross-disciplinary collaboration in healthtech innovation

What's next for MediVerse

  1. Expanding the computer vision model to detect a wider range of anomalies
  2. Integrating with Electronic Health Record (EHR) systems for seamless data flow
  3. Developing a mobile application for on-the-go access
  4. Implementing AI-driven predictive analytics for surgical outcomes
  5. Conducting clinical trials to validate the system's efficacy and gather user feedback
  6. Exploring partnerships with medical device manufacturers for direct endoscopy feed integration
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