AI in healthcare compliance checklist
Clinical AI sits at the intersection of medical-device law, health-data privacy, and patient-safety expectations — three of the strictest regimes there are. Whether your system triages symptoms, flags scans, or drafts clinical notes, the obligations differ sharply by intended use and jurisdiction. This checklist helps you scope that work: tell it what your system does and where it operates, and it surfaces the relevant requirements across classification, technical safeguards, clinical validation, and explainability.
The classification question that drives everything else
Whether your AI system qualifies as a medical device determines most of what follows. The test under EU MDR/IVDR and the FDA’s SaMD (Software as a Medical Device) guidance is primarily one of intended purpose:
- Software intended to diagnose, treat, prevent, monitor, or predict a disease or physiological state is generally a medical device.
- Administrative tools (scheduling, billing, transcription without clinical decision support) are generally not.
- General-purpose wellness tools (sleep tracking, mood logging without clinical claims) are usually not.
The critical word is “intended.” If your marketing material, labelling, or product documentation makes clinical claims, regulators will treat the software as a medical device even if you intended it as a wellness tool. Document and pin your intended purpose early — changing it after development is extremely costly.
The four compliance families
Once classification is determined, requirements cluster into four groups:
Device classification — under EU MDR, software is classified as Class I, IIa, IIb, or III based on risk. Most clinical decision support lands at IIa or above, requiring a Notified Body assessment. Under FDA, SaMD is classified by the significance of information it provides and the healthcare situation it operates in. The checklist surfaces the applicable pathway for your combination.
Data protection and technical safeguards — HIPAA’s Security Rule (US) mandates access controls, audit controls, integrity controls, and transmission security for electronic protected health information (ePHI). In the EU/UK, health data is Article 9 special-category data under GDPR requiring explicit consent or a specific public-health legal basis. Business Associate Agreements (BAAs) and Data Processing Agreements (DPAs) with vendors are non-negotiable.
Clinical validation — evidence that the system works as claimed on a population representative of your intended use. Both FDA and MDR expect prospective or retrospective studies showing clinical equivalence or superiority versus the comparator. Post-market surveillance (continued monitoring after deployment) is increasingly required, not optional.
Explainability and human oversight — clinical decision-support guidance in the EU and US increasingly expects clinicians to understand the basis of an AI recommendation so they can exercise independent judgment. Black-box outputs that drive clinical decisions without interpretability carry elevated regulatory and liability risk.
How it works
You select the system type (clinical decision support, diagnostic imaging, patient-facing triage, or administrative), the clinical use case, and the jurisdiction (EU, US, or UK). The tool assembles the applicable checklist from four families: device classification (EU MDR/IVDR, FDA SaMD), data-protection technical safeguards (HIPAA Security Rule, GDPR special-category data), clinical validation (evidence, intended-purpose definition, post-market surveillance), and explainability/human-oversight obligations. Tick items as you confirm them and the readiness score updates. Everything runs locally.
How to use it
- Pin your intended purpose first. Classification flows entirely from what you claim the system is for. A vague intended purpose creates the worst regulatory ambiguity.
- Do not skip the BAA / DPA. If any vendor touches patient data, the contract controls are as load-bearing as the technical ones.
- Validate against the real population. Clinical validation on a non-representative dataset is a leading cause of post-deployment failure and regulatory action.
- Keep a human in the loop. Most regimes expect a clinician to be able to understand and override the AI; design for that from the start.