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The legal field for AI in healthcare is a moving target, creating a new playbook for corporate defense as we speak. With automated systems now deeply embedded in how hospitals and insurers make decisions, companies getting sued are rolling out some creative arguments that all hinge on defining what “AI” even is. This word game has huge consequences for regulators, policymakers, and especially investors who are trying to figure out how much risk and liability is actually attached to these tools.

The Nuance of “Advisory” Versus “Algorithmic”

A go-to defense in AI lawsuits is to argue the tech is just ‘advisory’, it’s a tool that gives suggestions, it doesn’t call the shots. This difference matters because an advisory system keeps a human in charge of the final call, which can get the company off the hook for direct liability. We’re seeing this play out with UnitedHealth Group’s Optum subsidiary defending its nH Predict system. In current litigation, Optum’s lawyers are calling nH Predict “a guide” for doctors and families, insisting it’s “not a coverage-decision tool” Healthcare Finance News report on Optum’s nH Predict statements. The argument is that the system provides data, but actual people make the real decisions about patient care, turning nH Predict into a simple support resource. For anyone who follows policy, this walks a fine line between Clinical Decision Support (CDS) and full-blown Diagnostic AI. A tool that only ‘suggests’ can dodge the heavy-duty regulations that apply to a system making independent choices, which the FDA would likely regulate as a medical device.

The Bold Claim: “Not Powered by AI or an Algorithm”

Then there’s the much bolder strategy: just deny you’re using AI at all. Some companies in court are trying to get rid of the “AI” label completely, hoping to rebrand their software as a simple, old-school, rule-based process to duck the intense scrutiny that comes with advanced algorithms. Cigna is doing exactly this in the litigation over its PxDx system. The company has flatly stated that its PxDx system “is not powered by AI or an algorithm” but is just “a simple process” like the ones used by the Centers for Medicare & Medicaid Services (CMS) and other standard industry methods InsuranceNewsNet report citing Cigna’s PxDx statement. This is a deliberate attempt to change the conversation, arguing PxDx is just a conventional flowchart, not a complex machine learning model. For investors and policymakers, this is a big deal. If a court agrees that a tool automating thousands of complex decisions is “not AI,” it blows a massive hole in all the new regulations being built around AI liability. How companies talk about their own automation is now directly shaping what an “algorithm” even means in a lawsuit.

Why Disavow the Algorithm?

So why would a company go to all this trouble to deny its tech is an algorithm? It all comes down to a few key pressures, especially when you’re getting sued.

  • Regulatory Scrutiny: Slapping an “AI” label on a product is like painting a target on its back for regulators. It invites a much tougher review process, including things like proving Good Machine Learning Practice (GMLP) and having solid QMS / ISO 13485 certifications in place. If you can argue it’s not AI, you might just dodge that entire headache.
  • Liability Mitigation: There’s a growing push for “algorithmic accountability,” meaning developers are on the hook to prove their systems are fair and safe. If you successfully argue your system isn’t an algorithm, the whole legal basis for who’s at fault changes, potentially shielding the company from claims about bias or errors. It’s a get out of jail free card, legally speaking.
  • Public Perception: Let’s face it, AI has a PR problem. After all the news about AI spitting out health misinformation or chatbots going haywire, it can be good for business to say your product has nothing to do with “AI” and its perceived ethical baggage.
  • Definitional Ambiguity: Nobody has actually agreed on a single legal definition of “AI” or “algorithm,” and that chaos is a gift to corporate lawyers. They can exploit the fuzzy language, arguing that their automated system, however complex, doesn’t technically meet the legal bar for being called AI. This is exactly the kind of mess that judges like Dale A. Drozd, who is overseeing related cases, will have to sort out.

    Rule-Based Systems vs. Machine Learning: A Critical Distinction

    At the heart of these legal fights is the technical difference between old-school rule-based expert systems and modern machine learning models.

  • Rule-Based Systems: Think of these as simple flowcharts built on “if-then” logic that people write by hand. Their decisions are predictable and transparent, so they’re easy to check and explain. When Cigna calls its PxDx system a “simple process,” they’re trying to put it in this box, suggesting it’s just a set of auditable steps, not a self-learning machine.
  • Machine Learning Systems: These models aren’t programmed with explicit rules. They learn directly from data to spot patterns and make predictions which means their internal logic can be a “black box” that’s hard to interpret. They’re also vulnerable to “algorithmic drift” if the real-world data starts looking different from the data they were trained on. A company calling its system “a guide” is often admitting there’s an algorithm in there but trying to downplay its autonomy by highlighting the human in the loop. For anyone investing in these companies, this distinction is everything. A firm that claims its product isn’t AI could be admitting it’s using older, more rigid tech that might not keep up with competitors or build a real data moat. On the other hand, a company that’s upfront about using advanced AI, especially one with a formal SaMD (Software as a Medical Device) classification and a PCCP (Predetermined Change Control Plan), is presenting a totally different risk and reward calculation, one that allows for constant product improvement.

    The Investor’s Lens: Liability, Regulation, and Valuation

    If you’re an investor, these arguments over what to call “AI” aren’t just lawyers playing with words. They hit the company’s valuation and risk profile directly.

  • Litigation Patterns: You have to watch how these AI-liability lawsuits play out. Seeing whether these definitional defenses actually work in court will tell you a lot about future regulatory crackdowns and potential financial hits. One court precedent could cause a domino effect, with a whole cluster of companies copying the winning legal strategy.
  • Regulatory Certainty: This constant argument over definitions creates what you could call ‘regulatory debt.’ The lack of clear rules just invites companies to play games with how they frame their tech to look less risky. We are starting to see some clarity from agencies like the FDA, which has been formalizing its GMLP principles and creating specific 510(k) Clearance or De Novo Classification paths for AI, and that certainty is good for both developers and the people funding them.
  • Due Diligence: Any investor doing technical due diligence needs to dig in and challenge a company’s claims about its algorithms. Just taking a CEO’s word that a product “is not AI” without looking at the system architecture, the data pipelines, and the actual decision logic is a massive failure of due diligence. When a company’s whole story is in question, things like how it generates and validates Real-World Evidence (RWE) become even more important to verify. The FDA even has guidance on RWE for medical devices that’s worth reading. The lawsuits over UnitedHealth Group’s nH Predict and Cigna’s PxDx really show where we’re at with AI in healthcare. You have UnitedHealth playing it safe, calling its system a simple guide, while Cigna goes for broke, claiming its system isn’t even an algorithm. These different defenses reveal just how strong the legal and PR pressures are for companies to control the narrative around their automated tools. For anyone in regulation, policy, or finance, these fights are about more than just one or two products. They are setting the ground rules for accountability and liability for the entire AI-powered healthcare industry. How these cases get resolved will absolutely define the future of both managing risk and investing in this space.

Frequently Asked Questions

How are companies attempting to redefine ‘AI’ in legal defense strategies?

Companies are employing two main strategies: framing their technology as merely ‘advisory’ rather than determinative, implying human oversight for final decisions, or aggressively claiming their systems are ‘not powered by AI or an algorithm’ but are instead traditional, rule-based processes. These approaches aim to mitigate liability and avoid heightened regulatory scrutiny associated with AI.

What is the significance of distinguishing between ‘advisory’ and ‘algorithmic’ systems for regulatory bodies and policymakers?

For regulatory bodies and policymakers, this distinction is crucial because an ‘advisory’ system, with a human in the loop, may fall under less stringent regulatory oversight than a system making independent determinations, which might be regulated as a medical device. This impacts the balance between clinical decision support and diagnostic AI, influencing regulatory frameworks.

What are the implications for investors if systems that automate complex decision-making can be legally classified as ‘not AI’?

If systems automating complex decision-making can be legally classified as ‘not AI,’ it could create a significant loophole in emerging AI-specific regulations and liability frameworks. This corporate framing directly impacts the perceived risk and accountability, potentially reducing a company’s exposure to claims arising from algorithmic bias or error, which investors track closely.

Why are companies motivated to disavow the ‘AI’ or ‘algorithmic’ label when facing litigation?

Companies are motivated to disavow the ‘AI’ or ‘algorithmic’ label to avoid more rigorous regulatory review, such as GMLP and QMS/ISO 13485 certifications, and to mitigate liability by shifting the legal framework for assigning accountability. Additionally, it can offer public relations benefits by distancing products from negative perceptions associated with AI health misinformation or chatbot failures, exploiting the current definitional ambiguity of AI.