Artificial intelligence(AI) is potentially changing how we diagnose, treat, and monitor patients.

From AI-powered 3D planning systems in orthopedics to algorithms that analyze X-rays for fracture detection, these technologies are becoming essential tools in modern medicine.

‍Machine learning models can now optimize implant positioning for joint replacements, and assess bone healing progress after orthopedic procedures.

WIth these technological advancements, the FDA is also taking a closer look at AI/ML-enabled medical devices, establishing new guidelines and expectations for companies seeking clearance.

One concept that's gaining particular importance in FDA submissions is AI explainability.

In this article, I outline key pitfalls to avoid when tackling AI explainability in regulatory submissions and provide actionable strategies to thoroughly address explainability concerns.

At Enhatch, we understand the regulatory landscape firsthand, having successfully navigated the FDA 510(k) clearance process for our patient-specific AI-driven instrumentation system for total knee arthroplasty.

Drawing from lessons learned during our own clearance process, these recommendations can help you build a more robust and compliant submission.

What exactly is AI explainability?

AI explainability refers to making artificial intelligence model decisions understandable to humans. Instead of treating your AI as a mysterious black box that produces outputs, explainability provides insights into how and why your model reaches specific conclusions.

Think of it this way: when a radiologist reviews an X-ray, they can point to specific areas and explain their reasoning. AI explainability aims to provide similar transparency for machine learning models.

In healthcare, explainability serves three critical purposes: ensuring patient safety, building trust with clinicians, and maintaining accountability throughout the care process.

Why is AI explainability important to the FDA?

The US FDA's interest in AI explainability stems from fundamental regulatory responsibilities. Patient safety remains the agency's top priority, and they need to understand how your model works to ensure safe outcomes for patients.

This focus on understanding AI-enabled devices has been formalized in the FDA's January 2025 draft guidance, which makes explainability a key element of their transparency framework for AI enabled medical devices. 

This regulatory shift requires comprehensive documentation in several areas. Manufacturers must document algorithmic decision processes, key input variables, breakdown scenarios, and operational constraints. The goal is to enhance user confidence and safeguard patients throughout the Total Product Life Cycle.

To understand why explainability has become so critical, it helps to examine the specific regulatory challenges the FDA faces when evaluating AI enabled medical devices.

Transparency enables proper risk assessment

FDA reviewers must evaluate whether your AI system poses acceptable risks compared to its benefits. Without understanding how your model makes decisions, this evaluation becomes nearly impossible.

Explainability supports bias detection and mitigation

The FDA recognizes that AI systems can potentially perpetuate or amplify existing healthcare disparities. Explainable models make it easier to identify when a system might be biased against certain patient populations. This also aligns with the FDA's Good Machine Learning Practice (GMLP) guidance. 

Explainable models make it easier to verify that your AI works well everywhere

The FDA needs confidence that your AI will perform consistently across different hospitals, patient populations, and clinical settings.

Clear communication of device scope and limitations ensures safe implementation 

The FDA requires that end users understand exactly what the AI device is designed to do and what it cannot do. This transparency about intended use, operational boundaries, and clinical limitations helps users make informed decisions about when to rely on AI recommendations versus when human judgment is essential. 

Clear scope definition prevents misuse and ensures the device is deployed within its validated parameters, directly supporting the GMLP principle that users must receive clear, essential information for safe operation.

Common FDA submission pitfalls for AI-enabled medical devices and how to avoid them

Submitting opaque black box models

This is one of the biggest mistakes medtech startups make. Even if your model performs well, the FDA will struggle to approve systems they can't understand. Build explainability into your development process from the beginning.

Over-relying on technical metrics without clinical connections

This can weaken your submission. For example, accuracy scores matter, but the FDA also cares about clinical relevance and real-world applicability.

Neglecting different user groups

Different user groups may have different needs and levels of technical expertise. Design your explainability features accordingly.

Treating explainability as an afterthought 

The most effective explainable AI systems integrate interpretability throughout the design and development process.

Not documenting explainability considerations throughout the development lifecycle 

This documentation becomes valuable evidence of your commitment to transparency and patient safety.

Failing to clearly communicate device scope to end users

Companies sometimes focus heavily on what their AI can do but inadequately address what it cannot or should not do. The FDA expects clear communication about the device's intended use, operational boundaries, and clinical limitations. 

Inadequate plans for ongoing explainability maintenance 

Many submissions lack comprehensive strategies for maintaining explainability throughout the device's lifecycle. 

The FDA expects detailed plans for routine monitoring of model performance, explanation quality, and user feedback to ensure explainability remains effective as the system evolves.

Also, do not assume that explainability requirements end at clearance. Post-market surveillance must include ongoing assessment of whether explanations remain accurate and useful to the end users.

How to address AI explainability in your FDA Submission

Review key FDA guidance documents 

Staying current with FDA guidance is an essential first step. The following documents provide foundational frameworks that medical device companies can incorporate into their AI development and validation processes:

• Good machine learning practice for medical device development
• Artificial intelligence in software as a medical device
• Transparency for machine learning-enabled medical devices

Focus on explainability early in the process

For medtech startups, investing in explainability early can accelerate your path to market. Rather than scrambling to add explanations after development or during a review period, build transparency into your core product strategy. Reduce rework during premarket submissions and provide confidence to end users as well as FDA reviewers about how the system makes decisions.

Provide a clear model description

• Start with comprehensive documentation of your AI system. Include detailed information about your model architecture, training data sources, input requirements, and expected outputs.
• Your submission should clearly describe model behavior patterns and performance characteristics. Avoid just providing accuracy metrics. Explain how your model typically responds to different types of input data.
• Document any preprocessing steps, feature engineering decisions, and validation approaches. 
• Use visual decision flowcharts for better interpretability.

FDA wants to understand your entire AI pipeline, not just the final model. Ensure your QMS documentation sufficiently covers the software description expectations from the “Content of Premarket Submissions for Device Software Functions” FDA Guidance.

Address clinical relevance

Connect your AI outputs to clinical reasoning processes. 

• Consider different user groups when explaining clinical relevance. For example, a system designed for radiologists may need different explanations than one intended for primary care physicians.
• Present evidence that users can trust and effectively use your AI outputs. 
• Include validation studies showing that users can understand your AI's recommendations and make good decisions based on them.

Highlight risk mitigation strategies

Detail how explainability supports detection of failure modes. Show how one can identify when your AI might be making errors or operating outside its intended scope.

• Connect explainability to your broader risk management strategy. 
• Demonstrate robust risk management frameworks that include automated detection systems with fail-safe mechanisms and human oversight protocols where qualified personnel review AI-generated recommendations.
• Discuss how interpretability supports cybersecurity measures, bias monitoring, and continuous model updates based on post-market data.

Regulators expect to see documentation around encryption protocols, access controls and regular software updates for cybersecurity measures taken.

Support Human-in-the-Loop use cases

Describe how users interact with your system throughout the process. Consider different levels of explanation for different user needs. Some users might want detailed technical explanations, while others prefer high-level summaries.

Be explicit with your automatic versus semi-automatic processes and algorithms so users understand their responsibility to interact with software outputs rather than blindly trust what is provided. Clarify how explainability features support clinical decision-making rather than replacing it.

Engage with the FDA early in your development process 

Pre-submission meetings provide opportunities to discuss explainability expectations and get feedback on your approach. Sooner engagements with FDA benefit you in the long run when it comes to building that line of understanding and trust with your device.

Final thoughts

AI explainability isn't just a regulatory checkbox. It's a design and safety imperative that builds trust with clinicians and patients.

Explainability bridges the gap between cutting-edge technology and clinical reality, ensuring that AI serves patients safely and effectively. Explainable AI systems also tend to be more robust and maintainable. The process of making your model interpretable often reveals important insights about its behavior and limitations.

By prioritizing explainability in your AI-enabled medical device, you're contributing to a more transparent, trustworthy future for healthcare AI.

Sources

• “Artificial intelligence in orthopaedic surgery” from the NIH website. Visit Page.
• “The future outlook for data in orthopedic surgery: A new era of real-time innovation” from the Sage journal website. Visit Page.
• “What is the role of explainability in medical artificial intelligence? A case-based approach” from the NIH website. Visit Page.
• “Good Machine Learning Practice for Medical Device Development: Guiding Principles” from the FDA website. Visit Page.
• “Artificial intelligence-enables device software functions: Lifecycle management and marketing submission recommendations” from the FDA website. Visit page.
• “Artificial intelligence in software as a medical device” from the FDA website. Visit Page.