Operating at Scale: Legal Considerations for Growth-Stage Health AI Companies
Bringing a Health AI product to market is a major achievement—but growing that product successfully requires more than just technical or clinical validation. As your Health AI company enters new markets, adds features, and pursues larger customers or investors, the legal landscape grows more complex. In this post, we outline the legal groundwork you’ve likely already laid—and the key areas to prioritize next to ensure compliance, accelerate sales, and prepare for strategic growth.
What You’ve Done So Far: Building a Solid Legal Foundation
You’ve likely spent months (or years) refining your offering, securing your first priced funding round, and launching into the market—whether through pilots with health systems or a direct-to-consumer model. To get here, your team has probably already tackled a few foundational legal areas:
Corporate Structure and Licensing
Virtual healthcare provider organizations are beginning to integrate AI into their platforms. Medical practices (whether virtual or brick and mortar) operating in multiple states must navigate “corporate practice of medicine” (CPOM) laws through the MSO-Friendly PC model and align licensure and scope-of-practice with their clinical operations. A compliant structure is essential for scaling without regulatory surprises.
Terms of Use and Privacy Policies
Your legal documents should be customized to your product, especially in today’s fast-changing privacy and AI regulatory landscape. Using “off-the-shelf” or general templated policies can expose you to liability and scrutiny from the Federal Trade Commission (FTC) and state regulators.
FDA Software Analysis (SaMD)
The FDA doesn’t just regulate physical devices—it also regulates software that functions as a medical device – many of which incorporate AI. Your team should have conducted a “Software as a Medical Device” (SaMD) analysis to assess whether FDA rules apply and what compliance steps are required.
Data Inventory and Training Data Rights
A robust data inventory tracks the origin, rights, and restrictions for all data used to train your AI model. This is crucial for managing privacy obligations, intellectual property rights, and vendor contracts.
What’s Next: Legal Levers for Sustainable Growth
As your business matures, you’ll likely face new legal and regulatory risks that can impact your speed to market, investor interest, and reputation. Here are five areas to focus on now to stay ahead:
1. Smarter Commercial Contracting
Your contracts shouldn’t just “cover the basics”—they should create trust, reduce risk, and speed up deals. Key provisions to review include:
IP ownership and licensing terms
Algorithm explainability and update responsibilities
Indemnification obligations
Pricing models and payment triggers
2. Navigating State-Level AI and Privacy Laws
States are moving quickly to regulate AI and health data. As you enter new markets or expand D2C offerings, keep track of:
Disclosure and transparency requirements
Consumer opt-out rights
Consent for sensitive health data use
3. Ongoing FDA Compliance
If your product evolves, your regulatory obligations might too. Known as “feature creep,” even seemingly small changes can trigger new FDA scrutiny. Build in regular checkpoints to assess whether updates could require new filings or impact reimbursement opportunities.
4. Marketing Reviews to Avoid Pitfalls
The FTC is closely watching how AI companies market their products. Avoid vague or unsubstantiated claims—especially those about clinical effectiveness, speed, or cost savings. Also, ensure endorsements and testimonials follow federal guidelines.
5. Privacy and Data Use in a Shifting Landscape
New state laws are creating HIPAA-like rules outside the traditional healthcare setting. Whether you’re handling consumer health data or covered entity data, take time to:
Review data sharing and de-identification practices
Map where data flows and how it’s stored
Update consents and disclosures where needed
6. Implementing an AI Governance Framework
As your AI product matures, establishing an internal AI Governance framework becomes essential. This includes defining accountability for model performance, ensuring explainability and bias mitigation, and tracking post-deployment monitoring. A strong governance structure not only supports regulatory compliance (with FDA, FTC, and state AI laws) but also builds trust with health system partners, investors, and patients.
Key Takeaways
As healthcare AI companies grow, legal issues become more strategic. The right legal infrastructure helps reduce delays in sales and due diligence, avoid state and federal enforcement actions, and build trust with users and partners. By proactively addressing the evolving legal landscape, your company can turn compliance from a burden into a growth enabler.
At Nixon Law Group, we specialize in helping healthcare innovators navigate complex regulatory frameworks and scale responsibly. Whether you’re preparing for your next funding round, expanding into new markets, or evolving your AI product, our team can help you build a legal foundation for long-term success. Please reach out to learn how we can support your next stage of growth.