Taiwan TFDA Announces Good Machine Learning Practice Principles for AI Medical Devices

On June 8, 2026, Taiwan's Food and Drug Administration (TFDA) announced the Good Machine Learning Practice (GMLP): Development and Management Principles for AI medical devices under Announcement No. FDA器字第1151603605.

The guidance adopts the ten lifecycle principles set out in the International Medical Device Regulators Forum's (IMDRF) "Good Machine Learning Practice for Medical Device Development: Guiding Principles." Although not legally binding, it provides a clear indication of the practices TFDA expects manufacturers to implement when developing, validating, and maintaining AI-enabled medical devices.

Manufacturers already following IMDRF GMLP principles are likely to find substantial alignment with TFDA's expectations, but should ensure their supporting documentation clearly demonstrates the relevance of clinical evidence to Taiwan where applicable.

TFDA Adopts IMDRF's Good Machine Learning Practice Principles

The guidance signals TFDA's expectation that AI-enabled medical devices should be governed throughout the entire product lifecycle rather than evaluated solely on algorithm performance at the time of initial authorization. Consistent with international regulatory trends, manufacturers should establish controls across the AI lifecycle, including:

• Intended use definition
• Software engineering and quality management
• Representative clinical datasets
• Risk management and human factors
• Clinical validation
• Post-market monitoring
• Management of model updates and retraining

Guidance Aligns with International AI Regulatory Frameworks

TFDA based the guidance on the International Medical Device Regulators Forum (IMDRF) publication "Good Machine Learning Practice for Medical Device Development: Guiding Principles." Furthermore, the guidance references several widely recognized international standards:

• ISO 14971
• ISO/IEC 23894
• ISO/IEC 42001
• ISO/IEC 5259
• AAMI TIR34971

The document also cites guidance issued by the U.S. FDA, Health Canada, and the United Kingdom's Medicines and Healthcare products Regulatory Agency (MHRA).

For manufacturers already implementing IMDRF GMLP or comparable international practices, the overall expectations should be familiar.

Key Commercial Implications

While the guidance does not establish new regulatory requirements, it highlights several areas that are likely to receive increased attention during product evaluation and throughout the product lifecycle.

Demonstrate Clinical Relevance for Taiwan

TFDA expects manufacturers relying on overseas clinical datasets to assess whether model performance can be generalized to Taiwan's patient population.

Where appropriate, applicants should be prepared to demonstrate that their training and validation datasets are representative of the intended clinical population and sufficiently independent to support reliable performance claims.

For products developed primarily outside Taiwan, this may require additional validation activities or supplementary clinical evidence before market entry.

Integrate AI Governance into Quality Management Systems

The guidance places significant emphasis on governance beyond initial model development.

Manufacturers should ensure AI-specific processes are embedded within existing quality management and compliance systems, including:

• Risk management
• Software engineering controls
• Dataset quality
• Documentation and traceability
• Human factors evaluation
• Cybersecurity

Rather than treating these activities as standalone AI initiatives, TFDA expects them to be integrated into an organization's broader quality management framework.

Prepare for Continuous AI Lifecycle Management

Manufacturers are expected to monitor real-world performance, identify model degradation, and maintain appropriate controls over software updates and model retraining throughout the product lifecycle.

The guidance specifically directs manufacturers to TFDA's existing guidance on Predetermined Change Control Plans (PCCPs), highlighting the importance of proactively managing anticipated software modifications while maintaining regulatory compliance.

Final Thoughts

The TFDA’s guidance provides a vital window into the agency’s expectations for AI medical devices. The core challenge for manufacturers is not necessarily preparing entirely new documentation but demonstrating that AI development, validation, post-market monitoring, and change management are supported by mature quality systems and evidence appropriate for Taiwan's clinical environment.

As regulatory expectations for AI-enabled devices continue to evolve across Asia, early alignment with these lifecycle governance principles is essential to mitigate long-term compliance risks. With a dedicated local office in Taiwan, Cisema is well-positioned to provide the on-the-ground support and strategic guidance you need to navigate these requirements.

For help aligning your organization’s AI medical device development and regulatory submissions with Taiwan's evolving expectations, contact Cisema today.

Further Information

• Explore Cisema’s services for medical device registration in Taiwan.

Reference

• Announcement of "Good Machine Learning Practices for AI Medical Devices: Development and Management
• Good Machine Learning Practice for Medical Device Development: Guiding Principles.