FDA Human Factors Expectations For AI-Enabled Medical Devices (2026): A Regulatory & Usability Framework

The rapid evolution of Artificial Intelligence (AI) in medical technology has fundamentally reshaped regulatory expectations. In January 2025, the U.S. Food and Drug Administration released draft guidance titled Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations, introducing detailed expectations for lifecycle management, transparency, usability engineering, and human factors documentation for AI-enabled device software functions (AI-DSF).

Although Human Factors Engineering (HFE) is not highlighted in the guidance title, the document clearly establishes enhanced regulatory scrutiny on usability, cognitive risk management, bias, AI transparency, and Human-AI team performance validation.

For manufacturers developing AI-enabled Software as a Medical Device (SaMD), machine learning (ML) algorithms, clinical decision support (CDS) tools, or adaptive AI systems, this guidance represents a significant regulatory shift in 2026 compliance strategy.

Executive Overview: Why This Matters in 2026

AI-enabled devices introduce:

• Adaptive learning systems
• Variable and evolving outputs
• Personalized recommendations
• Complex clinical workflow integration

Regulators now expect manufacturers to evaluate not just device performance, but the collaborative dynamics between humans and AI systems referred to as the Human-AI Team model.

This marks a transition from traditional usability validation to cognitive interaction validation in AI-regulated healthcare environments.

Key Human Factors Expectations for AI-Enabled Devices

1. Human-AI Team Dynamics: A New Usability Paradigm

The guidance introduces the concepts of:

• Human-Device Team
• Human-AI Team

This reframes the user from being a passive operator to an active decision-making collaborator alongside AI.

Manufacturers must now demonstrate:

AI outputs may include:

• Risk scores
• Diagnostic alerts
• Predictive analytics
• Confidence intervals

Regulators expect manufacturers to assess whether users:

• Correctly interpret AI outputs
• Understand model limitations
• Recognize uncertainty
• Apply outputs appropriately within clinical workflows

2. Use-Related Risk Analysis for AI Systems

The 2025 draft guidance significantly expands use-related risk management expectations.

Manufacturers must assess risks arising from:

• Misinterpretation of AI outputs
• Over-reliance (automation bias)
• Under-reliance
• Lack of contextual understanding
• Uncertainty in AI predictions
• Performance drift over lifecycle

AI-Specific Risk Considerations

Manufacturers may need to implement mitigations such as:

• Displaying confidence levels
• Alert prioritization logic
• “Check AI Output” prompts below threshold
• Override documentation tracking

These requirements align with 2026 regulatory trends emphasizing explainable AI (XAI), model transparency, and real-world performance monitoring.

3. Human Factors Validation Testing: Beyond Physical Tasks

While traditional HFE validation focuses on critical tasks, AI-enabled systems require evaluation of cognitive load, perceptual understanding, and real-world interpretation.

Manufacturers must validate:

• Whether clinicians understand why an alert was triggered
• Whether users interpret confidence scores correctly
• Whether AI explanations are meaningful
• Whether users appropriately override or question AI outputs

The guidance suggests comparative evaluation in certain cases:

Human-AI team performance vs. human-only device performance

Expanded Validation Elements

4. Documentation Requirements for Market Submissions

HF documentation must be included in the Device Description section of premarket submissions.

AI-enabled device submissions must describe:

• Device inputs (manual & automatic)
• AI model role in intended use
• Model inputs & outputs
• User characteristics
• AI workflow integration
• Automation level
• Calibration & configuration procedures
• Configurable AI elements
• Impact on clinical decision-making

AI-Specific Labeling Expectations

The guide recommends inclusion of a Model Card, detailing:

• Model training data characteristics
• Performance metrics
• Limitations
• Intended population use
• Known biases
• Clinical integration considerations

Transparency is now a regulatory expectation not a design preference.

User Interface Design for AI-Enabled Devices

Appendix B of the guidance outlines detailed UI expectations:

• Clear communication of AI role
• Presentation of uncertainty
• Appropriate information timing
• Cognitive load management
• Effective data visualization
• Confidence score representation
• Risk-based alert hierarchy

2026 UI Design Priorities

• Explainable AI dashboards
• Trust calibration mechanisms
• Context-sensitive alerts
• Bias disclosure mechanisms
• Adaptive interface safety controls

The interface must be balanced:

Building trust while preventing over-reliance.

Regulatory Impact in 2026

This guidance aligns with broader regulatory trends including:

• AI lifecycle management controls
• Continuous learning system oversight
• Real-world performance monitoring
• SaMD premarket documentation updates
• Risk-based AI governance frameworks

Manufacturers must now treat human factors engineering as an integrated AI lifecycle discipline not a standalone usability activity.

How Maven Regulatory Solutions Supports AI-Enabled Device Compliance

Maven Regulatory Solutions provides end-to-end regulatory consulting for AI-enabled medical devices, including:

• AI-specific human factors strategy development
• Use-related risk analysis for AI output
• Human-AI team validation protocol design
• Cognitive risk mitigation planning
• Regulatory submission documentation support
• FDA pre-submission strategy
• AI transparency & labeling compliance review
• Lifecycle management & post-market monitoring strategy

Our regulatory experts align usability engineering, AI governance, and regulatory submission strategy to ensure compliant, audit-ready, and submission-ready documentation.

Frequently Asked Questions (FAQ)

1. Does the FDA require Human Factors testing for AI-enabled devices?

Yes. While not explicitly titled as HFE guidance, the draft document clearly requires expanded usability validation including cognitive risk evaluation and Human-AI team performance.

2. What is automation bias in AI medical devices?

Automation bias occurs when users overly rely on AI recommendations without independent verification, potentially increasing patient safety risk.

3. Is a Model Card mandatory?

While presented as recommended, strong transparency documentation is expected in 2026 submissions, especially for AI/ML-enabled SaMD.

4. Does this apply to Software as a Medical Device (SaMD)?

Yes. The guidance directly impacts AI-enabled device software functions, including ML-based SaMD and adaptive systems.