RadVision

RadVision: AI-Powered Imaging & Predictive Insights for Smarter, Faster Emergency Care

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Inspiration

The need for faster, more accurate emergency diagnostics in high-pressure environments inspired the creation of RadVision. During mass casualty incidents (MCIs), radiologists and emergency teams face immense challenges in prioritizing critical cases, impacting mass casualty preparedness and response. By integrating AI-driven imaging analytics with FHIR-based real-time data sharing, I envisioned a solution that enhances emergency imaging, triage, and seamless coordination among healthcare providers.

What it does

RadVision is an innovative AI-powered radiology app that integrates with the MeldRx FHIR system alongside RapydAid and CareExchange apps to provide seamless emergency triage and imaging diagnostics. It automates MRI segmentation, CT scan detection, and severity assessment, enabling radiologists to identify critical conditions faster. Key functionalities include:

  • Automated AI-based imaging analysis for pneumonia and other severe conditions.

  • FHIR API for real-time EHR integration, ensuring rapid data exchange.

  • Interoperability with RapydAid to receive pre-hospital patient data from emergency responders.

  • Integration with CareExchange, allowing healthcare providers to access radiology reports instantly.

How I built it

  • FHIR Interoperability: We used CDS hooks and FHIR API standards to enable secure, standardized data sharing.

  • AI-Powered Imaging: Implemented deep learning model for CT scan anomaly detection.

  • Seamless App Ecosystem: Built RadVision as part of the MeldRx FHIR system, alongside RapydAid (for real-time triage) and CareExchange (for provider collaboration).

  • Cloud Deployment: Developed RadVision for web-based radiology use cases, ensuring privacy, security, and scalability.

Challenges I ran into

  • Real-time data fusion: Integrating multi-source clinical and imaging data while maintaining FHIR compliance.

  • AI Optimization: Running deep learning model efficiently for rapid MRI segmentation in hospital environments.

  • Interoperability & Compliance: Ensuring that the FHIR API aligns with EHR standards and regulatory frameworks (B11 compliance for EHRs).

  • Scalability for MCIs: Handling mass casualty scenarios where thousands of patient records need to be processed simultaneously.

Accomplishments that I'm proud of

  • Seamless triage-to-imaging workflow: Successfully linked RapydAid, RadVision, and CareExchange into a unified platform.

  • AI-Powered MRI & CT Analysis: Achieved high-accuracy detection for pneumonia condition.

  • FHIR-Enabled Real-Time Data Sharing: Built a fully interoperable system for instant patient data exchange between first responders, radiologists, and healthcare providers.

  • Scalability for Mass Casualty Events: Designed a system capable of handling emergency surges and optimizing resource allocation.

What I learned

  • FHIR standardization is key: Ensuring consistent, secure data sharing across different healthcare stakeholders is critical for real-time decision-making.

  • Predictive AI can detect imaging conditions that may be missed by the human eye, even by experienced radiologists. AI-powered imaging analysis, particularly deep learning models like convolutional neural networks (CNNs), has been shown to outperform or complement radiologists in several key areas.

  • Interoperability challenges exist: Even with FHIR compliance, integrating with legacy EHRs posed significant hurdles.

What's next for RadVision

  • Expanding AI capabilities to detect additional conditions (e.g., stroke, cancer, and musculoskeletal disorders, tumors, cardiac).

  • Improving mobile accessibility for on-the-go radiologists and emergency personnel.

  • Enhancing predictive analytics to provide personalized risk scores based on historical patient data.

Potential Impact

RadVision addresses a critical need in emergency medicine by providing faster, AI-powered imaging diagnostics. Its seamless integration with RapydAid and CareExchange ensures that patient data flows efficiently from emergency response teams to radiologists and ultimately to healthcare providers, improving patient outcomes and reducing diagnostic delays.

Creativity and Originality

RadVision stands out by:

  • Combining AI-powered imaging with real-time FHIR interoperability.

  • Bridging emergency response, radiology, and healthcare provider coordination.

  • Automating MRI segmentation & CT scan analysis to prioritize high-risk cases faster.

  • Addressing mass casualty triage through its scalable, edge-AI-powered approach.

Documentation Thoroughness and Transparency

RadVision’s FHIR API compliance ensures full B11 alignment with EHR standards. I document all source attributes, model decision-making processes, and risk factors to adhere to the FAVES principles—ensuring Fairness, Appropriateness, Validity, Effectiveness, and Safety in AI-driven healthcare solutions.

With RadVision, RapydAid, and CareExchange working together in the MeldRx FHIR system, transforming emergency medicine, radiology, and healthcare collaboration—one AI-powered diagnosis at a time.

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