Covers hospitals, clinics, primary care, digital health platforms, medical device manufacturers, in vitro diagnostics, pharmaceutical AI, clinical decision support, radiology AI, digital pathology, surgical robotics, remote patient monitoring, mental health technology, and health insurance claims AI. Any AI system that influences clinical decisions, patient triage, diagnosis, treatment selection, or medical device functionality falls within this overlay..
Organisations in healthcare face AI obligations that generic templates don’t cover — clinical-safety duties, sector-specific regulators, data protection expectations for the populations you serve, and emerging AI-specific legislation. Blanket policies written for software companies miss most of what matters.
The AI Risk Register produces a pre-scored AI risk register tailored to your jurisdiction, risk appetite, and the specifics of healthcare. It is a drafting aid built to accelerate — not replace — qualified review by your in-house practitioners or external counsel.
Drawn from published evidence and regulatory guidance specific to healthcare. Each is pre-scored on a 5×5 likelihood × impact matrix in the Risk Register tool and referenced in the generated policy.
An AI diagnostic or clinical decision support system produces an incorrect output — a missed malignancy, contraindicated drug recommendation, or false-negative screening result — that a clinician acts upon without sufficient independent verification, causing delayed, omitted, or incorrect treatment and direct patient harm.
AI systems trained on historically unrepresentative datasets produce systematically less accurate outputs for underrepresented populations — including women, Black, Asian, and minority ethnic patients, elderly individuals, and patients with disabilities — leading to inequitable diagnostic accuracy, risk stratification, and treatment recommendations that perpetuate existing health disparities.
A clinical AI model validated at deployment progressively deteriorates in real-world performance due to changes in patient population demographics, clinical workflows, disease prevalence, or medical imaging equipment, without the degradation being detected through post-market monitoring, resulting in a sustained period of substandard AI-assisted clinical care.
Clinical staff accept AI recommendations without applying adequate independent clinical judgment — particularly in high-volume settings using AI for triage or worklist prioritisation — resulting in systematic failures to catch AI errors that a vigilant clinician would have identified, and misallocation of clinical attention toward AI-flagged cases at the expense of AI-missed cases.
A healthcare AI system is trained or fine-tuned on patient health records, diagnostic images, or genomic data without adequate legal basis under GDPR Article 9 or HIPAA — for example by treating routine clinical data as available for commercial AI training without explicit consent — exposing the organisation to regulatory enforcement, patient trust damage, and potential criminal liability.
Large language model-based AI tools used for clinical documentation, patient communication, or treatment protocol retrieval generate plausible-sounding but factually incorrect medical information — including fabricated drug interactions, incorrect dosage guidance, or invented clinical evidence — that enters the clinical record or influences treatment without being identified as erroneous.
Outputs support, rather than replace, the qualified practitioners in your healthcare team. Human review is treated as a core step, not a rubber stamp.
Before any AI system is acted on in healthcare, it is tested in the specific population, workflow, and risk context of your organisation — not just in a vendor's demo environment.
Outputs carry enough context — regulatory references, assumptions, known limitations — that a reviewer in healthcare can trace and challenge them.
Named roles — named individuals, named committees — are accountable for the AI decisions that affect people in your healthcare organisation.
Performance is reviewed across the demographic groups your healthcare organisation actually serves, not just a representative-of-the-dataset average.
You select jurisdiction, industry, and risk appetite. The tool produces an XLSX register pre-populated with 12 to 15 AI risks relevant to your sector — each already scored on a 5×5 matrix with suggested mitigations.
The workbook is designed for review inside your existing risk-management process: add organisation-specific risks, adjust scores, assign owners, and set review cadence. The starting point is a credible draft, not a blank template.
The output is a draft calibrated to healthcare — it still requires review by qualified in-house or external practitioners before adoption.
Selected obligations the tool’s output references for healthcare. This is not a complete statement of your legal obligations — qualified counsel should verify applicability in your jurisdiction and context.
EU
AI systems intended to be used as safety components of medical devices regulated under EU MDR 2017/745 and IVDR 2017/746 are classified as high-risk under EU AI Act Annex III §1, capturing AI-powered diagnostics, clinical decision support, predictive risk scoring, AI-driven drug dosing, and any AI embedded in a regulated medical device.
EU
Regulation (EU) 2017/745 governs all medical devices placed on the EU market, including software that qualifies as a medical device (SaMD). Under MDCG 2019-11 guidance, AI/ML software intended to diagnose, prevent, monitor, predict, prognose, treat, or alleviate disease typically qualifies as SaMD requiring CE marking.
EU
Regulation (EU) 2017/746 governs IVD medical devices, including AI software used to analyse diagnostic specimens such as digital pathology slides, genomic sequencing outputs, and laboratory test results. AI-powered digital pathology tools and AI interpreting laboratory data for clinical purposes are captured under this regulation.
US
The FDA regulates AI/ML-based SaMD in the United States through its 510(k), De Novo, and PMA pathways. FDA's 2023 Marketing Submission Recommendations and Predetermined Change Control Plan (PCCP) framework govern how AI medical software is reviewed, approved, and updated post-market.
Every output is an editable draft. Every section carries the regulatory basis it was built from, so reviewers in your healthcare team can verify, challenge, and adapt it to local context. Nothing is a finished legal instrument; nothing is intended to bypass qualified review.
We publish explicit disclaimers in the generated documents themselves, and treat human oversight as a default — not an opt-in. The tool’s role is to reduce the time your qualified practitioners spend on the first draft, so they can focus on review and adaptation.
Review a sample of what the tool produces, then generate a draft tailored to your own healthcare organisation. $19.95 · one-time.
26 regulations across 8 jurisdictions. This list is descriptive, not exhaustive, and is subject to change — verify applicability with qualified counsel before relying on any reference.