Bioexcelife
AI in healthcare is no longer just about innovation. It is now about governance, traceability, and long-term control.
That is why the new draft from the International Medical Device Regulators Forum (IMDRF) deserves close attention. In April 2026, IMDRF opened public consultation on its draft Technical Framework for Artificial Intelligence Life Cycle Management, with comments accepted from 10 April to 10 July 2026. The draft is meant to support a harmonized approach to AI-enabled medical devices across the full product lifecycle.
Why this draft matters now
This is not just another AI policy discussion. The draft sets out how manufacturers should think about AI-enabled medical devices from planning and design all the way to deployment, monitoring, updates, and eventual retirement. IMDRF describes it as a framework to support the secure, safe, ethical, and effective lifecycle management of AI-enabled medical devices, while complementing existing IMDRF work on software, SaMD, cybersecurity, and Good Machine Learning Practice.
For manufacturers, that matters because global regulators often move in the direction IMDRF signals. A draft like this does not create law by itself, but it often shapes future regulatory expectations across multiple markets.
The biggest shift: from approval mindset to lifecycle mindset
The most important message in the draft is simple: AI-enabled medical devices cannot be managed as one-time submissions.
Instead, IMDRF frames AI oversight as a total lifecycle responsibility. The draft covers planning, data collection and management, model building and tuning, verification and validation, clinical evaluation, deployment, operations and monitoring, real-world performance evaluation, and sunsetting.
That changes the conversation. It means the key question is no longer just, “Did the model perform well during development?” The real question becomes, “Can the manufacturer continue to control, monitor, and justify performance after deployment?” That is a much higher bar.
Data quality is no longer a side issue
One of the strongest themes in the draft is data governance.
IMDRF gives special attention to dataset quality, representativeness, traceability, and bias mitigation. The document highlights risks linked to training data bias, poor curation, and data issues that can affect real-world performance, especially across different patient groups or use settings. It also emphasizes data lineage and documentation so manufacturers can trace issues back to root causes when problems arise post-market.
That is a major signal for the industry. In the next phase of AI regulation, weak data governance will be seen as a regulatory risk, not just a development weakness.
Monitoring after launch is becoming central
Another key message is that AI performance cannot be assumed to remain stable after launch.
The draft stresses continuous monitoring, including logging, performance tracking, anomaly detection, and incident handling. It also addresses real-world performance evaluation as a distinct activity, focused on structured analyses of safety, effectiveness, and clinical utility in actual use.
IMDRF also calls attention to model drift, noting that AI-enabled medical devices can degrade over time as real-world inputs shift away from the data used during development. The draft points to the need for drift detection, alerting, and, where appropriate, recalibration or retraining under proper control. It specifically notes that this concern is especially important for generative AI-enabled medical devices.
This is one of the most practical takeaways for manufacturers: approval is no longer the finish line. Ongoing performance stewardship is.
Risk-based control will define good AI governance
The draft also reinforces a risk-based approach across the lifecycle. It integrates quality management, risk management, cybersecurity, and human oversight as core lifecycle concepts rather than side topics. In practice, this means controls, validation depth, and monitoring intensity should match the intended use and risk profile of the AI-enabled device.
That approach aligns well with where regulators are already heading. Higher-risk AI in healthcare will increasingly be expected to show stronger governance, tighter evidence, and more robust lifecycle controls.
What manufacturers should do next
For medtech companies, this draft is a chance to act before expectations harden.
Now is the time to review whether your AI development process really covers the full lifecycle. Does your team have strong data traceability? Can you justify dataset quality and bias controls? Do you have a workable plan for post-deployment monitoring, drift detection, and change control? Can your quality system support these activities in a structured way? Those are the kinds of questions this framework pushes to the front.
Companies that start building these controls now will be in a stronger position as the global regulatory environment matures. Those that still treat AI documentation as a one-time submission package may find themselves struggling to keep up.
Final thought
The IMDRF draft is important because it reflects a bigger regulatory shift.
AI-enabled medical devices are no longer being judged only by what they can do at launch. They are being judged by how responsibly they are governed over time.
That is the future of AI compliance in medtech: not just innovation, but lifecycle accountability.
How Bioexcel can help
At Bioexcel, we help manufacturers prepare for evolving regulatory expectations in AI-enabled medical devices, SaMD, and global medtech compliance. From lifecycle strategy and technical documentation to risk-based planning and regulatory alignment, we support a smarter path to market readiness.
