Using AI to Interpret Lab Results? Here’s When It Becomes a Regulated Medical Device
- Kristina Romanenko
- 1 day ago
- 7 min read
Biotech and longevity companies use AI mainly to analyze complex biological data—like blood panels, genomics, proteomics, or microbiome results—and turn them into insights about health, aging, or disease risk. These AI systems can find hidden patterns, estimate “biological age,” predict how someone might respond to a therapy, or recommend lifestyle or treatment options.
Powerful, yes. It’s also the exact scenarios that make founders and legal teams start asking:
If we use an LLM to explain lab results, do we become a regulated medical company overnight?
The answer is rarely about the technology itself. It’s the intended use and the impact of your AI that determines how regulators will treat your product, and whether FDA clearance or CE-marking becomes mandatory.
The First Question Every Team Must Ask
Before building or releasing an AI feature, ask:
Is this AI-enabled software intended to diagnose, treat, prevent, mitigate, or inform clinical management of a disease or condition?
❌ If the answer is NO, your AI likely sits in the wellness or informational space. You can craft beautiful, insightful AI experiences without medical-device paperwork — though you should still be careful about claims and messaging.
✔️ If the answer is YES, even implicitly, your AI feature becomes a medical device. Not because it uses AI, but because it performs a medical function. The moment your assistant interprets biomarkers in ways that suggest identifying, ranking, or ruling out disease, you’ve crossed the regulatory line.
Longevity products live at the edge of this line. A biological-age report is typically outside regulatory scope. A model that ‘screens for early liver disease’ or ‘suggests likely dysbiosis’ is not.
If you want a deeper dive into where this line is drawn in Europe, we break it down in our guide to EU MDR/IVDR cybersecurity requirements for medical and diagnostic software.
Understanding this distinction is critical: it’s not the AI itself that triggers regulation, but the intended use and the clinical impact of its outputs. This clarity will guide your team in assessing risk, planning validation, and determining whether FDA clearance or CE-marking is required before deployment.
When AI Crosses the Line Into Medical Device Regulation
Understanding exactly when AI falls under AI medical device regulation is crucial for biotech and longevity teams building features that interpret lab results or biomarkers.
Stepping Into Regulated Territory
Once an AI feature is considered medical, it follows the same regulatory journey as any other medical device. The next step is a rigorous risk assessment:
How significant is the information your AI provides to healthcare decisions, and in what clinical context?
This assessment, combined with the intended medical use, defines the device’s risk class and sets the regulatory path.
Across all classifications, certain foundational controls are mandatory:
Quality Management System (ISO 13485): A robust framework ensures development, testing, and maintenance processes are consistent, auditable, and repeatable.
Software Development Lifecycle (IEC 62304): Every stage of software design, implementation, verification, and maintenance must be documented and controlled.
Risk Management (ISO 14971): Developers must identify and mitigate hazards, from inaccurate predictions to software failures.
Labeling and Documentation: Clear instructions, limitations, and intended use statements ensure safe deployment and regulatory clarity.
Cybersecurity Controls: Medical devices must implement robust cybersecurity measures —penetration testing, vulnerability scanning, access controls, encryption, and de-identification. AI introduces new risks, requiring these controls to be tailored for model-specific vulnerabilities.
The guiding principle is simple: the higher the potential impact on patient care, the stricter the controls. Even if your AI only augments human decision-making, regulators expect a level of rigor proportionate to the risks your software could introduce.
How AI Changes the Regulatory Equation
AI doesn’t just inherit the baseline rules of SaMD — it introduces its own set of challenges.
Machine learning systems, especially those interpreting patient-specific biomarkers, are dynamic. Unlike traditional deterministic software, their performance can shift over time because of retraining, new data, or updated model parameters. This creates a regulatory problem: how do you control a product that evolves after approval?
In both the U.S. and EU, significant changes that affect the safety, effectiveness, or intended use of a medical device trigger regulatory reassessment before the updated device can be marketed.
Recognizing the challenges of evolving AI, the FDA introduced the Predetermined Change Control Plan (PCCP). This framework allows manufacturers to specify in advance which types of AI changes they plan to make and how those changes will be controlled to maintain safety and effectiveness
A PCCP must describe:
What changes will be allowed (e.g., retraining, new datasets, model refinements).
How those changes will be assessed through predefined validation protocols.
How safety will be preserved (rollback plans, version control, monitoring).
How traceability and documentation will be maintained for regulatory review.
Rather than freezing your model after clearance, a PCCP lets you predefine and manage specific modifications — but only if the scope, boundaries, and verification methods are rigorously established in advance. [1]
We break down how HealthTech teams can design safe, compliant AI systems in our guide to building secure GenAI architectures for HIPAA and GDPR environments.
Beyond PCCP, AI also amplifies traditional requirements in several critical ways.
Validation is no longer a one-time effort — it becomes continuous. You must test your models against diverse patient populations, edge cases, and potential biases, documenting limitations and failure modes.
Transparency is critical: regulators expect sufficient interpretability or explanatory material so that users and reviewers understand what the model can and cannot do, confidence levels, and any assumptions behind predictions. For black-box models, like third-party LLMs, this often means adding interpretability layers or guardrails to ensure outputs are safe for clinical interpretation. [2]
Cybersecurity in AI-Enabled Medical Devices
Medical devices have long been subject to cybersecurity requirements. The FDA’s “Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions” reaffirmed that any software-enabled or networked medical device must meet security objectives — including authenticity, authorization, confidentiality, availability, and secure updateability. [3]
AI introduces new dimensions to that challenge. Foundation-models, machine learning pipelines, or third-party LLMs can be vulnerable to data poisoning, model inversion or theft, adversarial inputs, data leakage, bias exploitation, and performance drift — risks that traditional software does not encounter.
While the FDA’s current guidance does not explicitly reference AI, these vulnerabilities must be proactively addressed. Sponsors are expected to demonstrate that their cybersecurity strategy accounts for AI-specific risks.
In practical terms, security-by-design remains essential, but for AI it must explicitly address AI-specific vulnerabilities across the entire product lifecycle. From the earliest design stages, developers should implement:
AI-focused threat modeling to anticipate unique risks.
Vulnerability assessments covering data integrity, model theft, and adversarial inputs.
Secure architecture that safeguards patient data, model integrity, and confidentiality.
Software Bill of Materials (SBOM) to track all components, including third-party and open-source libraries powering the AI.
Secure update and patching plans, accounting for model evolution due to retraining or environmental changes.
In other words, AI raises the bar for cybersecurity. Meeting baseline requirements isn’t enough. To satisfy regulators and ensure patient safety, you must explicitly address AI‑specific vulnerability vectors, embed security throughout the product lifecycle, and be ready to demonstrate that your AI will remain safe, private, and reliable under realistic use and hostile conditions. [4]
Many of these risks show up in real biotech systems. In our Biotech Cybersecurity Report 2025, we analyzed 50 platforms and uncovered common issues like insecure APIs, credential leaks, outdated software, and misconfigured environments that could directly impact AI pipelines.
Being Non-Medical Isn’t a Free Pass
Even if your AI-enabled feature does not qualify as a medical device, you are not exempt from legal and regulatory obligations. In both the U.S. and Europe, there is growing scrutiny over AI software, regardless of medical claims. In the U.S., the Federal Trade Commission (FTC) requires that AI systems be accurate, reliable, and free from unfair or deceptive practices. In Europe, the EU AI Act is creating a framework for responsible AI, classifying high-risk AI applications and imposing obligations for transparency, risk management, and human oversight.
The practical implication is clear: even if you don’t need FDA clearance or CE-marking, your company can still face penalties, reputational damage, or enforcement action if your AI produces misleading, unsafe, or biased outputs. Compliance doesn’t disappear just because the product is labeled “wellness” or “informational.”
At a minimum, startups should implement robust governance for risk assessment, validation, documentation, and transparency. This includes:
Clear communication to users: define what the AI can and cannot do, limitations of the outputs, and any assumptions behind predictions.
Ongoing monitoring: even non-medical AI can drift, producing outputs that are unexpected or potentially misleading.
Bias and fairness checks: ensure your AI doesn’t inadvertently reinforce inequities or produce harmful recommendations.
Cybersecurity hygiene: safeguard underlying data and models against unauthorized access, tampering, or exploitation.
We outline how biotech teams can assess these risks in practice in our guide to penetration testing for genomic and biomarker platforms.
Self-Assessment Compliance Checklists
Understanding where your AI-enabled product sits on the regulatory and risk spectrum is not always obvious — especially for longevity and biotech teams operating close to the boundary between wellness, diagnostics, and regulated medical software.
To help teams gain clarity early, we’ve created a set of practical self-assessment checklists used by founders, product leaders, and compliance teams to evaluate regulatory exposure, cybersecurity maturity, and compliance readiness across key frameworks.
Each checklist is designed to surface gaps before regulatory reviews, partnerships, or audits force reactive fixes.
Review your product development lifecycle against FDA Secure Product Development Framework (SPDF) guidance, including secure design, risk management, vulnerability handling, and post-market cybersecurity expectations for software-enabled and AI-driven medical devices.
Evaluate your administrative, technical, and physical safeguards — and identify common HIPAA pitfalls that frequently arise in AI-enabled data pipelines, cloud environments, and third-party integrations.
Assess your software or device against EU MDR and IVDR cybersecurity requirements, from design and validation through post-market monitoring and incident response.
Review your technical security controls against ISO 27001 Annex A requirements, including access control, encryption, logging, vulnerability management, and secure software development practices.
These assessments are particularly useful for teams building AI features that interpret biological data, generate health insights, or operate in regulated or near-regulated environments.
Conclusion: AI in Longevity Must Be Built With Regulatory Awareness
AI enables longevity and biotech companies to translate complex biological data into actionable insights at unprecedented speed. But power comes with responsibility: regulatory scrutiny depends on the intended use and potential impact on health.
For AI features that qualify as medical devices, structured risk assessment, continuous validation, transparency, and AI-specific cybersecurity measures are essential to ensure patient safety, maintain regulatory compliance, and protect trust.
Even non-medical AI is not exempt. Frameworks like the FTC guidance in the U.S. and the EU AI Act require accuracy, fairness, transparency, and ongoing monitoring. Ignoring these obligations can lead to safety risks, biased outputs, or regulatory enforcement.
Ultimately, responsible AI starts with rigorous risk governance and security built in from day one. Companies that embed these practices gain not only compliance but also a competitive edge: regulators, users, and investors increasingly gravitate toward AI systems that are safe, reliable, and resilient.
