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..
What this page draws on for healthcare compliance.
Organisations in healthcare face AI obligations that generic templates don’t cover — clinical-safety duties, medical-device conformity assessment, and patient-data protection, 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 Bias Audit Framework produces an intake-driven AI bias audit framework 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.
The Bias Audit Framework tiers AI use-cases by risk and applies fairness testing protocols with explicit thresholds — calibrated to the discrimination and disparate-impact risks specific to healthcare.
In healthcare, the two AI risks most directly within the AI Bias Audit Framework's remit are “Demographic Bias Producing Disparate Clinical Outcomes Across Patient Groups” and “AI Model Drift Causing Undetected Real-World Performance Degradation”. Both are surfaced in the canonical sector overlay we ship as healthcare primary evidence — not generic AI risks dressed up with sector vocabulary.
The AI Bias Audit Framework produces an intake-driven AI bias audit framework that addresses these risks head-on, pre-aligned to the regulators most active in healthcare, and structured so your in-house practitioners can adapt rather than start from a blank page. The output is an AI-assisted draft intended for review by qualified healthcare practitioners before adoption.
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.
Pick jurisdiction, industry, and risk appetite.
Under a minute of structured questions.
AI produces your jurisdiction-specific document in under five minutes.
Qualified review, then download as .docx, .xlsx, or .pptx.
You describe your organisation and AI estate, then answer 25 self-assessment questions across four phases (use-case characterisation, current bias-testing maturity, governance posture, and a 5-question sector-specific block tailored to HR / Healthcare / Financial Services / Government / Education / Insurance / Universal). The tool maps your stated posture into a structured, evidence-based bias audit framework ready for your compliance, legal, and AI-governance practitioners.
The Executive Summary Word document is a one-page sign-off artifact — Risk Classification Scorecard, top 5 bias risks tied to specific AI systems, 30/90/365-day path forward, sign-off block, embedded heatmap + doughnut + gauge charts. The detailed Excel workbook is the working remediation instrument: tier each AI tool, pre-filled audit checklist, fairness testing protocol with explicit thresholds, RACI ownership, permitted/prohibited lists, monitoring cadence, action plan, and a live Dashboard. Both are AI-assisted drafting aids intended to accelerate review by qualified practitioners.
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.
A draft-ready AI usage policy.
A pre-scored AI risk register.
Plain-language AI guidelines for staff.
An AI vendor assessment aligned to ISO 42001 or NIST AI RMF.
A gap analysis against the EU AI Act, NIST AI RMF, or ISO 42001.
A schedule-based GDPR Art.28(3)-aligned Data Processing Agreement for AI vendors.
A P1–P4 severity-classified AI incident response playbook.
A board-ready AI governance programme with org chart, RACI, KPIs, and 90-day roadmap.
Review a sample of what the tool produces, then generate a draft tailored to your own healthcare organisation. $39 · 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.
Yes. Published clinical evidence shows AI trained predominantly on lighter Fitzpatrick skin-tone images underperforms on melanoma detection and pressure-injury identification in darker-skinned patients, and pulse-oximeter algorithms systematically overestimate blood-oxygen saturation in Black and Asian patients. The FDA’s 2023 SaMD marketing-submission recommendations require performance testing across demographically diverse patient datasets before clearance, and EU AI Act Annex I (medical-device AI, high-risk from 2 August 2028 under the Digital Omnibus’s deferral) adds an Article 10 bias-examination duty. The Bias Audit Framework runs stratified performance testing across the Fitzpatrick scale rather than a single aggregate accuracy score.
Yes — AI cardiac-risk prediction and ECG-interpretation models trained predominantly on male patient data have been shown to systematically underestimate cardiovascular risk and misclassify atypical presentations more common in women, including atypical myocardial-infarction symptoms, and frequently exclude pregnant patients from validated reference ranges. Neither the FDA’s SaMD framework nor EU AI Act Annex I mandates a specific sex-disaggregation test, but both require demonstrated performance across the intended-use population as a condition of clearance/conformity. The Bias Audit Framework runs sex-disaggregated performance analysis as a named test, not an assumed pass.
Yes — AI that is a safety component of a device regulated under the EU MDR or IVDR is EMBEDDED high-risk under Article 6(1) and Annex I (not the Annex III standalone track), requiring conformity assessment, Article 11 technical documentation, Article 12 event logging, and Article 14 human oversight enabling clinicians to override the AI, additive to existing MDR/IVDR conformity. The adopted Digital Omnibus deferred this Annex I embedded application date from 2 August 2027 to **2 August 2028**. The Bias Audit Framework’s Article 10 bias-examination protocol is built for that 2028 date, not an earlier one.
"FDA cleared" means the device went through the 510(k) notification pathway and demonstrated substantial equivalence to a predicate device — it is not the same as "FDA approved," which applies to the PMA pathway. Clearance requires performance testing across demographically diverse datasets and, since the December 2024 Predetermined Change Control Plan (PCCP) final guidance, a pre-specified plan for which future AI updates are pre-approved without a new submission. Clearance is a floor, not a bias audit: the Bias Audit Framework’s stratified testing is designed to sit on top of FDA clearance, not substitute for it.
Yes — population-health, readmission-risk, and acuity-scoring AI frequently uses socioeconomic proxies (insurance status, postcode, employment type, prior utilisation) that correlate with race and ethnicity, producing risk scores that direct clinical resources toward higher-income or majority-ethnic patients even though race is never an explicit input. Neither HIPAA nor the FDA’s SaMD framework specifically tests for proxy discrimination — it has to be tested for directly. The Bias Audit Framework screens for these proxy variables rather than only checking whether a protected characteristic is a named field.
Yes, in most cases. A covered entity must have a signed Business Associate Agreement with any AI vendor or auditor that accesses protected health information before granting access, and the underlying use must independently be permissible under the HIPAA Privacy Rule — a BAA alone does not authorise an otherwise-impermissible use. Bias-audit data is commonly run instead on data de-identified to the Safe Harbor or Expert Determination standard under §164.514 to avoid a new BAA. The Bias Audit Framework’s intake step flags which path a given audit needs before analysis starts.