Digital Health and the FDA: When Software Becomes a Medical Device

Once upon a time, the FDA regulated things you could hold — pills, scalpels, pacemakers. Today, it regulates code.

Artificial intelligence, machine learning, and cloud-based analytics have turned algorithms into diagnostic tools, therapy aids, and even treatment decision engines. That’s revolutionized healthcare — and upended how the law defines a “medical device.”

For digital health innovators, this is where ambition meets regulation. Because once your software crosses a certain line — from supporting care to delivering it — you’re no longer just a tech company. You’re a medical device manufacturer.

Here’s what that means, and how to stay on the right side of FDA oversight.

What Is Software as a Medical Device (SaMD)?

The FDA defines Software as a Medical Device (SaMD) as software “intended to be used for one or more medical purposes that perform these purposes without being part of a hardware medical device.”

In plain English: if your app, algorithm, or platform is intended to diagnose, cure, treat, mitigate, or prevent disease, the FDA may consider it a medical device — even if it’s just running on a smartphone.

Examples of SaMD:

• An AI tool that detects diabetic retinopathy from retinal images.

• A mental health app that claims to “treat” anxiety using cognitive behavioral therapy modules.

• A wearable that predicts arrhythmias and alerts users to seek care.

Not SaMD:

• A step counter or sleep tracker that promotes wellness.

• A scheduling app for patient visits.

• A software platform that helps doctors manage electronic health records.

The distinction lies in intended use — what your product claims to do. And in the FDA’s eyes, your marketing language matters as much as your code.

How the FDA Classifies SaMD

SaMD isn’t one-size-fits-all. The FDA classifies devices (and software) into three categories based on risk:

1. Class I (Low Risk):

   • Administrative or general wellness tools.

   • Examples: Fitness apps, medication reminders.

2. Class II (Moderate Risk):

   • Software that provides diagnostic or treatment recommendations but leaves final decisions to clinicians.

   • Examples: Radiology image analysis software, AI that assists in cancer detection.

3. Class III (High Risk):

   • Software that directly drives clinical decisions or performs critical diagnostic functions.

   • Examples: AI that autonomously diagnoses conditions or adjusts medication dosage.

Each class comes with different levels of FDA scrutiny. Class I devices are often exempt from premarket review. Class II devices typically require 510(k) clearance (proof they’re substantially equivalent to an existing approved device). Class III devices require Premarket Approval (PMA) — the gold standard of FDA review.

The FDA’s New Approach: Lifecycle Oversight

Traditional medical devices are static — once approved, they don’t change. But software evolves constantly.

To handle that, the FDA has adopted a Total Product Lifecycle (TPLC) approach for SaMD, emphasizing continuous monitoring and real-world performance data instead of one-time approvals.

Key components of this model include:

• Good Machine Learning Practices (GMLP): Standards for data quality, model training, and validation.

• Predetermined Change Control Plans: Developers can make future software updates without re-submitting to FDA, as long as they specify how updates will be validated and monitored.

• Postmarket Performance Reporting: Ongoing monitoring to ensure updates don’t introduce safety or accuracy risks.

The goal is agility — balancing innovation with accountability.

Why This Matters for Innovators

For startups and digital health companies, FDA regulation can feel intimidating — but it’s also a mark of credibility. Increasingly, hospitals, payors, and investors want to see FDA clearance before adopting clinical-grade AI or diagnostic software.

Here’s what early-stage teams should focus on:

1. Clarify Your Intended Use

Your labeling and marketing language define whether you’re in FDA territory. Words like “diagnose,” “treat,” or “prevent” can trigger regulation — while “support,” “track,” or “inform” typically do not.

2. Know When to Engage the FDA

If you’re unsure whether your product qualifies as SaMD, consider a Pre-Submission Meeting through the FDA’s Q-Submission Program. It’s a structured way to get early feedback from regulators before committing to a full clearance pathway.

3. Document Everything

From data provenance to algorithm updates, maintain traceable documentation. The FDA values process discipline — not just performance claims.

4. Design for Compliance Early

Building FDA compliance into your software architecture and QA systems from day one is cheaper than retrofitting it later.

5. Don’t DIY Regulation

SaMD regulation is nuanced. Work with regulatory counsel or a qualified consultant early. They’ll help you map your classification, documentation, and submission strategy to fit your product roadmap.

Where the FDA Is Headed Next

As AI continues to advance, the FDA is rethinking its role — moving from gatekeeper to governor of evolving algorithms. In 2024, the agency announced new guidance for “adaptive AI” systems — software that learns from new data in real time.

Future regulations will likely include:

• Expanded postmarket monitoring.

• More transparency around algorithmic bias and training data.

• International harmonization through the International Medical Device Regulators Forum (IMDRF).

The bottom line? The FDA wants to promote innovation — but not at the expense of patient safety or public trust.

Final Thoughts

The line between healthcare technology and medical device is blurring fast. For innovators, that’s both a challenge and an opportunity.

The best digital health companies aren’t waiting for the FDA to catch up — they’re designing for regulation, treating compliance as part of their competitive advantage.

Because in a field where lives are on the line, the question isn’t just can your product work? It’s can it prove that it works — safely, reliably, and responsibly?

And that’s the difference between software that’s just smart, and software that’s FDA-cleared.

This article is for informational purposes only and does not constitute legal advice. Reading or relying on this content does not create an attorney–client relationship. For guidance specific to your product or compliance strategy, consult qualified legal counsel experienced in FDA and digital health regulation.