Traditional medical software has been built on static models, validated once and unchanged in production. AI/ML systems, however, are increasingly continuously learning, adapting to new data post-deployment.
While this enhances performance and personalization, it also introduces regulatory risk: changing model behavior can alter safety and efficacy. Regulators now need frameworks that accommodate evolving intelligence, not just snapshot validation.
The new generation of AI-driven tools are continuously learning and adapting to new data long after deployment. This shift introduces enormous promise for improving clinical outcomes but also challenges the long-standing regulatory model of “approve once and monitor occasionally.”
Continuous Learning Models vs. Locked Algorithms
Historically, regulatory frameworks were designed around software systems that did not change after approval. These locked algorithms made validation straightforward: once approved, the model as-is, with minimal post-market concern about changes in behavior.
But modern AI systems, particularly those using machine learning, operate differently. They are designed to learn from real-world data in real time, refining their outputs to improve accuracy and relevance. While this improves their clinical utility, it also creates potential for unpredictable behavior – especially if the system begins adapting based on biased, incomplete, or unprepresentative data.
This continuous evolution undermines the traditional one-time evaluation model, forcing regulators to reimagine how safety, efficacy, and transparency are maintained over time.
Lifecycle-Based Oversight (FDA TPLC + RWE Frameworks)
FDA’s Total Product Life Cycle (TPLC)
The FDA’s TPLC framework applies throughout a product’s entire lifecycle, from pre-market development to post-market monitoring. With continuous learning AI, manufacturers must include Predetermined Change Control Plans (PCCP) outlining how and when model updates will occur, ensuring planned changes get regulatory visibility upfront.
But modern AI systems, particularly those using machine learning, operate differently. They are designed to learn from real-world data in real time, refining their outputs to improve accuracy and relevance. While this improves their clinical utility, it also creates potential for unpredictable behavior – especially if the system begins adapting based on biased, incomplete, or unprepresentative data.
This continuous evolution undermines the traditional one-time evaluation model, forcing regulators to reimagine how safety, efficacy, and transparency are maintained over time.
Real-World Evidence (RWE) Framework
The FDA’s RWE program encourages using real-world data to continuously evaluate safety and efficacy. For adaptive models, this means tracking outcomes in clinical settings, detecting performance drift, and feeding that data into updates and regulatory oversight.
Real-Time Monitoring Requirements Under FDA and EU
FDA
FDA draft guidance demands structure enabling near real-time monitoring of AI systems, with automated alerts for drift, degradation, or unpredicted behavior. Monitoring systems must be part of submissions early, with clear plans for data collection, metrics, and thresholds for action.
EU AI Act + MDR/IVDR
Effective August 2024 (with high-risk provisions by August 2026), the EU AI Act treats medical AI as high-risk and requires:
- Continuous post-market surveillance
- Automated logging of all decisions, incidents, and drift metrics
- Monthly performance reviews, with serious incident reports within 15 days
Regulatory teams must, therefore, embed logging infrastructure, performance dashboards, and defined escalation paths into QMS.
Explainability, Bias Detection & Algorithm Transparency
As oversight becomes more continuous, regulators are also emphasizing the need for transparency, especially when it comes to how AI models make decisions. Both the FDA and EU authorities are encouraging or requiring explainability features that allow clinicians, auditors, and regulators to understand how a decision is made, what data influenced it, and whether the model’s behavior is consistent with its intended use.
Closely tied to explainability is the need for bias detection and mitigation. Adaptive models trained on real-world data can easily inherit and amplify existing inequities if not properly controlled. Regulators are calling for proactive measures to detect and reduce bias, including pre-deployment testing with diverse datasets and ongoing monitoring for performance across patient subgroups.
Transparency also extends to development practices: developers must maintain thorough documentation of their training data, versioning history, risk mitigation strategies, and quality management processes.
Regulators emphasize that adaptive AI must be auditable, explainable, and bias-mitigated:
- Explainability: Systems must expose interpretable decision logic or rationale so humans can understand how outcomes were reached.
- Bias Detection: Pre-deployment testing and post-market monitoring must include protocols to identify and mitigate bias – especially for underrepresented subgroups.
- Transparency: Full traceability is required – capturing model versioning, training data provenance, drift logs, updates, and risk assessments. This feeds into Technical Documentation and QMS audit trails.
What This Means for Regulatory Teams
Regulatory teams at medtech and AI-health firms must now:
- Design robust PCCPs for adaptive models that anticipate change and define triggers, thresholds, and validation pathways.
- Integrate real-time monitoring systems, dashboards, alerts, performance & bias metrics, into both pre- and post-market operations.
- Ensure explainability tools and audit logs are embedded in software and QMS.
- Adapt Technical Documentation and QMS to track evolving models (version histories, risk logs, bias analysis, and training data lineage.
- Build regulatory capacity by training staff on AI oversight, perform internal/external audits, and establish protocols aligned with FDA and EU requirements.
How RegDesk Operationalizes Continuous Oversight
AI’s regulatory challenges demand AI-powered solutions. At RegDesk, we’ve developed a regulatory intelligence platform that directly supports the lifecycle oversight models emerging from both FDA and EU authorities.
Our AI capabilities include:
- Submission Authoring: Leveraging ML to match and pre-populate submission content based on previous documentation cutting months off development cycles.
- Real-Time Document Compare: Automatically highlighting changes in updated regulations and guidance documents.
- Regulatory Intelligence Gathering: Monitoring 124 global markets and using AI to deliver timely insights.
- Secure AI Architecture: RegDesk ensures that all AI functions are instance-specific, protecting customer data integrity while still delivering scalable intelligence.
These tools help regulatory teams move beyond manual processes and into a future-ready model of continuous, proactive compliance.
Conclusion
The rise of continuously learning AI is rewriting the rules of medical device regulation. The one-and-done approval model is no longer fit for purpose in a world where algorithms evolve daily.
Instead, regulators are adopting frameworks grounded in lifecycle oversight, real-world evidence, and real-time monitoring. They are prioritizing transparency, explainability, and fairness as cornerstones of regulatory compliance.
For regulatory professionals, this means adapting quickly. Developing a strong PCCP, implementing robust monitoring systems, and maintaining transparent, auditable documentation are no longer optional.
At Regdesk, we help companies operationalize these requirements through a comprehensive regulatory intelligence platform that supports lifecycle submissions, real-time monitoring integration, and global compliance alignment. As regulators evolve, so must our tools, processes, and mindsets. The future of regulation is continuous, just like the algorithms it now oversees.
