Parachute provides governance infrastructure specifically designed for clinical Artificial Intelligence deployments in healthcare settings. The platform automates the evaluation, approval, and continuous monitoring of AI models to ensure compliance and mitigate risks like bias and performance drift. This results in faster, safer integration of medical AI while generating necessary auditable documentation trails for regulatory scrutiny.
Funding
Funding not disclosed
Founders
Product
Problem
Hospitals face significant challenges in safely and compliantly adopting artificial intelligence (AI) technologies, leading to deployment delays and potential risks. Evaluating AI vendors and managing the lifecycle of deployed AI systems requires extensive manual effort and specialized expertise, creating bottlenecks for innovation.
Solution
Parachute offers an AI governance platform designed for healthcare institutions to streamline the adoption of AI. The platform facilitates the evaluation and deployment of AI solutions through clinically-informed workflows and automated compliance checks against industry standards like HAIP, NIST AI RMF, and ISO 42001. It provides comprehensive lifecycle management, including a centralized AI project registry and real-time performance monitoring via MLOps integration. Parachute enhances transparency with AI "nutrition labels" and a shared AI risk registry, fostering trust and enabling proactive risk mitigation.
Target Audience
The platform targets hospitals and healthcare providers seeking to implement AI governance, manage AI vendor relationships, and ensure compliance with regulatory frameworks.
Features
- Clinically-informed AI vendor evaluation frameworks aligned with HAIP, NIST AI RMF, ISO 42001, and FDA SaMD compliance templates.
- Centralized AI Project Registry for cataloging and tracking ML models and AI systems.
- AI "Nutrition Labels" based on ONC's 31 transparency attributes for clear communication of AI use case value.
- Shared AI Risk Registry for collaborative learning and management of AI-related risks.
- Automated vendor vetting processes for pre-screening third-party AI tools.
- Real-time AI performance monitoring through MLOps integration with automated update triggers.
- AI copilot functionality for automating documentation and compliance tasks.
- Cross-functional collaboration tools with decision audit trails for transparency and accountability.
- Performance analytics and reporting capabilities for tracking AI system effectiveness over time.
- Integration capabilities with existing hospital technology stacks.