AI/ML-Enabled Medical Devices
SFDA approach to AI/ML devices
The SFDA regulates AI and machine learning-enabled medical devices under the existing Medical Devices Law and MDS-REQ 1 framework. There is no separate AI-specific regulation (unlike the EU AI Act), but the SFDA follows IMDRF and international guidance on AI/ML SaMD.
Classification of AI/ML devices
Classification follows the standard SFDA rules, based on:
- The intended purpose of the AI function (diagnostic, therapeutic, monitoring)
- Seriousness of the condition the device addresses
- Whether the output drives independent clinical decisions or is reviewed by a clinician
High-risk AI/ML devices (e.g. autonomous diagnostic systems for life-threatening conditions) are likely Class C or D and require the most comprehensive technical files.
Technical file requirements for AI/ML devices
In addition to standard SaMD requirements, AI/ML devices should address in the technical file:
- Algorithm description — training data, model architecture, intended population
- Validation and verification — test dataset performance, generalisability across populations
- Transparency and explainability — how clinicians can interpret model outputs
- Bias and fairness assessment — performance across demographic subgroups
- Cybersecurity risk — specific to AI model integrity and adversarial attacks
- Version and change management — how algorithm updates are controlled
Predetermined Change Control Plans (PCCPs)
For adaptive AI/ML algorithms (those that learn or update after deployment), the SFDA is expected to align with IMDRF guidance on Predetermined Change Control Plans (PCCPs) — pre-agreed protocols describing the types of changes the algorithm may undergo without requiring a new MDMA submission. This is an emerging area and manufacturers should monitor SFDA guidance updates.