Why Are AI-Enabled Function Devices Difficult to Regulate?

The rapid advancement of Artificial Intelligence (AI) has transformed healthcare, with modern medical devices leveraging AI-driven software to optimize performance in real time and enhance patient care. While AI enables continuous improvement within a Total Product Lifecycle (TPLC) regulatory framework, it also presents challenges for regulators like the U.S. Food and Drug Administration (FDA) in ensuring safe and effective use throughout the device’s lifecycle.

Top Three Challenges in Regulating AI-Enabled Devices

The FDA has released a variety of information focused on the safe development and usage of AI in medical device software, highlighting the agency’s focus on a TPLC approach to AI, providing industry software developers with guiding principles for Good Machine Learning Practices (GMLP), along with insight into the challenges they face in regulating AI-enabled software function devices. 

The complexity of AI learning presents significant regulatory challenges, as its full mechanisms are not yet fully understood. Below are three key challenges the FDA faces in ensuring the safety and effectiveness of AI-enabled software function devices.

1. Lack of Transparency ("Black Box" Problem)

Many AI/ML models, especially deep learning and neural networks, operate as "black boxes," meaning their decision-making processes are not easily interpretable by humans.

This makes it difficult for regulators, clinicians, and patients to understand how the AI system arrives at its conclusions, raising concerns about trust, accountability, and liability.

2. Risk of Bias & Poor Generalization 

AI models rely on training data, and if the data lacks diversity or is biased, the model may produce inaccurate or unfair results, particularly when applied to new populations or clinical environments.

AI models trained in controlled settings may fail to generalize when deployed in real-world conditions, leading to misdiagnosis or inappropriate treatment recommendations.

Hallucination in AI (particularly in Generative AI, GenAI): AI models sometimes generate false, misleading, or entirely fabricated outputs that appear credible but lack a factual basis. This poses a serious risk in medical applications, where incorrect AI-generated diagnoses or recommendations could lead to misguided treatments, patient harm, or legal liability.

3. Continuous Learning & Post-Market Monitoring Challenges

Unlike traditional medical devices that remain static after approval, some AI models evolve over time through self-learning mechanisms, software updates, or integration with external data sources.

This dynamic nature complicates regulatory approvals, as a model that was once safe and effective could change in unpredictable ways post-deployment.

A Brief History of Lifecycle Management

The concept of Lifecycle Management (LCM) has been pivotal in software development since the 1960s. It provides a structured framework for planning, designing, developing, testing, integrating, deploying, maintaining, and eventually retiring software. Modern Software Development Lifecycles (SDLCs) embody these principles, offering a systematic approach to reliable software delivery.

To address the unique challenges of AI, the FDA’s Digital Health Center of Excellence (DHCoE) introduced the AI Lifecycle (AILC) model. This framework, described below in Figure 1, maps the phases of traditional SDLCs to the specific needs of AI software development, incorporating technical and procedural considerations at every stage.

Figure 1. AILC Model

Current FDA Frameworks for AI Regulation 

The FDA employs several frameworks to regulate AI-enabled devices, each serving a distinct role:

Total Product Lifecycle (TPLC) Approach

The TPLC approach provides a comprehensive framework for overseeing medical devices from development through commercialization. By integrating premarket and postmarket data, the FDA can make informed decisions to ensure device safety and effectiveness throughout its lifespan. This approach supports timely responses to safety issues and fosters consistent regulatory practices.

Good Machine Learning Practice (GMLP) Guiding Principles

In collaboration with international regulatory bodies, the FDA has outlined guiding principles for Good Machine Learning Practice (GMLP). These principles promote the development of safe, effective, and high-quality AI/ML-enabled medical devices by addressing key areas such as:

• Transparency: Ensuring developers clearly document and communicate the design and intended use of AI models.
• Performance Monitoring: Continuously assessing real-world performance to identify and address any issues.
• Risk Management: Proactively mitigating risks associated with AI algorithms, including bias and data vulnerabilities.

Adhering to GMLP supports the continuous improvement of device performance while safeguarding patient safety.

Conclusion 

Regulating AI-enabled medical devices is a complex and evolving challenge. The FDA’s efforts to adapt its frameworks—through initiatives like the Pre-Cert Program, TPLC approach, and GMLP guiding principles—highlight its commitment to balancing innovation with patient safety. As AI continues to transform healthcare, collaboration between regulators, manufacturers, and stakeholders will be essential to addressing these challenges effectively.

Look to Rook for AI Development & Regulatory Expertise

Rook Quality Systems supports AI-enabled device manufacturers by providing expert guidance on regulatory pathways, good machine learning practices, and aligning MLOps with IEC 62304. Our services include dataset quality assessments, configuration management plans, and design control support to ensure compliance and successful market entry. Trust us for comprehensive solutions tailored to AI device development.