Project Info
The Problem Medical professionals often work under grueling schedules that can lead to fatigue and performance decline. Overworked surgeons may inadvertently skip critical mental checklists meant to ensure patient safety. Despite the success of initiatives like the WHO Surgical Safety Checklist—which has been proven to reduce post-surgical mortality by 36.6% when properly implemented—traditional paper-based systems see a completion rate of just 37% during surgical signouts. However, studies show that digital checklist aids can push compliance close to 100%. This gap underscores an urgent need: leveraging digital technology not only to support surgical safety but also to empower medical residents with real-world practice and continuous feedback. Our Solution We built a multimodal surgery data collection and observability platform designed specifically for white-collar healthcare operations. Our platform captures everything that happens in the operating room—integrating vision and audio data—to ensure surgical practices are safe, compliant, and educational. Key features include: Comprehensive Data Integration: Our system collects and synthesizes multimodal data from surgeries, acting as an advanced digital assistant. It aids surgeons in real-time compliance, replacing the need for traditional medical scribes and live EHR systems. Comprehensive Data Integration: Our system collects and synthesizes multimodal data from surgeries, acting as an advanced digital assistant. It aids surgeons in real-time compliance, replacing the need for traditional medical scribes and live EHR systems. Educational Enhancement: Addressing the shortage of real-world surgical practice for medical residents, our platform acts as an autonomous end-to-end educational assistant. It provides live feedback during surgery and generates comprehensive post-operation reports, with both qualitative and quantitative assessments. Educational Enhancement: Addressing the shortage of real-world surgical practice for medical residents, our platform acts as an autonomous end-to-end educational assistant. It provides live feedback during surgery and generates comprehensive post-operation reports, with both qualitative and quantitative assessments. Foundation for AI-Driven Training: The anonymized, aggregated data forms the basis for training models on successful surgeries, creating opportunities for continuous learning and improvement. By ensuring that data is aggregated at scale without compromising individual anonymity, we face minimal resistance from stakeholders. Foundation for AI-Driven Training: The anonymized, aggregated data forms the basis for training models on successful surgeries, creating opportunities for continuous learning and improvement. By ensuring that data is aggregated at scale without compromising individual anonymity, we face minimal resistance from stakeholders. How We Built It Our development journey was rooted in the challenge of achieving seamless multimodal data interoperability. We conducted extensive trial and error, fine-tuning models for each type of data through rigorous A/B testing to perfect our project flow. Key technical highlights include: Data Fusion in the OR: We set out to capture a diverse range of data—visual, audio, and video—from the operating room, wanting our system to provide both real-time and post-operation analysis. Data Fusion in the OR: We set out to capture a diverse range of data—visual, audio, and video—from the operating room, wanting our system to provide both real-time and post-operation analysis. Conversational Voice Assistance: Leveraging Eleven Labs, we built an augmented voice agent that interacts with surgical staff. This agent is supported by advanced search tools like Perplexity Sonar, which provide contextual awareness and sophisticated reasoning. Conversational Voice Assistance: Leveraging Eleven Labs, we built an augmented voice agent that interacts with surgical staff. This agent is supported by advanced search tools like Perplexity Sonar, which provide contextual awareness and sophisticated reasoning. Real-Time Compliance and Reporting: Our platform not only monitors compliance before, during, and after surgery but also delivers real-time conversational feedback. Post-surgery, it generates detailed reports that offer: An overview of the procedure (both pre-surgery and during surgery) Scoring metrics for educational assessment. Actionable insights for improvement. Real-Time Compliance and Reporting: Our platform not only monitors compliance before, during, and after surgery but also delivers real-time conversational feedback. Post-surgery, it generates detailed reports that offer: An overview of the procedure (both pre-surgery and during surgery) Scoring metrics for educational assessment. Actionable insights for improvement. By integrating various AI tools with a deep understanding of the surgical environment, our platform enhances patient safety, streamlines surgical workflows, and transforms the educational landscape for future medical professionals.
Challenges we ran into
Integrating many different APIs into one project Infrastructure for collecting data for AI agents was limited and hard to find Designing a functioning front-end with almost zero front end experience Being forced to pivot and change our idea halfway through the hackathon
Accomplishments we're proud of
Worked with multiple modalities (video, audio, vitals, text, images) Integrated an end-to-end product for the whole surgical process, from pre-surgery, to during, to post-surgery. Successfully built a functional MVP within 36 hours!
What we learned
How to integrate multiple APIs efficiently The importance of UX design for user experience Team collaboration and time management in an environment where we are forced to iterate and fail over and over again in a short period of time. User Interviews Conducted user interviews with multiple doctors and learned that extending our medical alerts system to monitor patient vitals would be ideal. This is why we started working with Terra API and incorporated sample data in order to simulate monitoring vitals and queue alerts based on this data. Acquired one LOI from a doctor holding purchasing power in his hospital (note: this hospital is based outside of the US).
What's next
Inject compliance requirements from major hospital chains Run a pilot study Refine and fine-tune AI models for better latency. Incorporate real-life and real-time patient vitals into our solution.
Surgentic - AI-Powered Surgical Safety Assistant
Surgentic is a cutting-edge AI assistant designed to enhance surgical safety and compliance. By combining real-time voice interaction, screen recording, and automated checklist management, Surgentic ensures confidence and safety in every surgical procedure.
🌟 Features
- Real-time AI voice interaction during procedures
- Automated surgical safety checklist management
- Screen recording and session playback
- Beautiful, modern interface with dark mode
- Compliance report generation
- Persistent conversation history
- Real-time transcription
- Responsive design optimized for medical environments
🚀 Tech Stack
- Framework: Next.js 15
- Styling: Tailwind CSS with shadcn/ui
- Database: Supabase
- Animation: Framer Motion
- Voice AI: ElevenLabs
📦 Installation
- Clone the repository:
git clone https://github.com/yourusername/surgentic
cd surgentic
- Install dependencies:
pnpm install
- Create a
.envfile in the root directory with the following variables:
NEXT_PUBLIC_SUPABASE_URL="your-supabase-url"
NEXT_PUBLIC_SUPABASE_ANON_KEY="your-supabase-anon-key"
XI_API_KEY="your-elevenlabs-api-key"
AGENT_ID="your-elevenlabs-agent-id"
- Run the development server:
pnpm dev
- Open http://localhost:3000 with your browser to see the result.
🏗️ Project Structure
/app- Next.js app directory containing routes and layouts/components- Reusable React components/lib- Utility functions and shared logic/public- Static assets and recorded sessions
🔑 Environment Variables
NEXT_PUBLIC_SUPABASE_URL: Your Supabase project URLNEXT_PUBLIC_SUPABASE_ANON_KEY: Your Supabase anonymous keyXI_API_KEY: ElevenLabs API keyAGENT_ID: ElevenLabs agent ID
🏥 Features in Detail
Real-time Voice Interaction
Surgentic provides seamless voice communication during surgical procedures, allowing hands-free interaction with the AI assistant.
Surgical Safety Checklist
An interactive checklist based on WHO Surgical Safety standards, ensuring all critical steps are followed and documented.
Screen Recording
Built-in screen recording capabilities allow for procedure documentation and later review, enhancing training and quality assurance.
Compliance Reporting
Automated generation of compliance reports based on checklist completion and procedure documentation.
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🙏 References
- Next.js Documentation
- Tailwind CSS
- Supabase Documentation
- ElevenLabs Documentation
- WHO Surgical Safety Checklist
👥 Contributing
Contributions are welcome! Please read our Contributing Guide for details on our code of conduct and the process for submitting pull requests.
Analysis
View
Metric
- 8
- 5
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- CSSIn code
- Next.jsIn code
- ReactIn code
- SupabaseIn code
- Tailwind CSSIn code
- TypeScriptIn code
- Google GeminiClaimed
- JavaScriptClaimed
- OpenAIClaimed
- PythonClaimed
6 of 10 appear in the indexed code. 4 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
136 KB
Source files
30
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
SeanZCai/treehacks-agentic-surgeon
39 files · 1.3 MB · @ 625b1ba
Structure
Interface
13 files · 33%Screens, components and styles rendered to the user.
+1 moreAPI & routing
4 files · 10%Request entry points: routes, handlers and controllers.
Application logic
10 files · 26%Domain rules, services and shared utilities.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- YAML50%
- TypeScript33%
- CSS14%
- Markdown2%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
package.json
npm · 22- @11labs/react
- @neondatabase/serverless
- @supabase/supabase-js
- dotenv
- framer-motion
- motion
- next
- react
- react-dom
- react-feather
- sonner
- supabase
- +10 more
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
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