Each submission to the 2nd Washington Hackathon 2026 will be evaluated across multiple dimensions to identify the most innovative, impactful, and feasible AI-powered solutions addressing our healthcare challenges.

Idea submissions close September 17, 2026  |  Presentations September 19–20, 2026 at WUST, Alexandria, VA

Innovation & Technical Excellence
25%

Evaluates the uniqueness and technical sophistication of the AI solution compared to existing healthcare approaches.
  • Novel application of AI/ML technologies
  • Technical sophistication and quality of algorithms
  • Advances beyond current healthcare solutions
  • Innovative use of data and analytics

Healthcare Impact
25%

Assesses the potential of the solution to address significant healthcare challenges effectively.
  • Demonstrated clinical value and health outcomes
  • Addresses critical healthcare needs
  • Potential for improving patient care quality
  • Impact on healthcare accessibility or efficiency

Implementability & Feasibility
20%

Evaluates whether the solution can be realistically implemented in healthcare settings.
  • Technical feasibility and readiness level
  • Integration with existing healthcare systems
  • Consideration of regulatory requirements
  • Practical implementation roadmap

Scalability & Adoption
15%

Assesses how well the solution can scale across healthcare environments.
  • Potential for widespread clinical adoption
  • Cost-effectiveness and resource efficiency
  • Business model and sustainability
  • Adaptability across different healthcare settings

Presentation & Demo
10%

Evaluates how well the team communicates and demonstrates their healthcare AI solution.
  • Clear demonstration of AI functionality
  • Quality of data visualization and UX/UI
  • Compelling presentation of clinical applications
  • Technical explanations and Q&A responses

Ethics, Privacy & Safety
5%

Assesses the solution's ethical considerations, data privacy practices, and patient safety.
  • Data privacy and security measures
  • Bias detection and mitigation strategies
  • Explainability of AI decision-making
  • Consideration of patient safety and ethical use