Sources & methodology

Content is built from primary legal and official texts, recognised consensus and reporting guidance, scholarly literature, and vendor technical documentation where we are explaining a specific product behaviour or specification. We do not rely on news articles, press releases or marketing claims as evidence of safety or effectiveness.

This material is educational: it is not patient-specific medical advice and not legal advice.

1. Primary law and official texts — e.g. EUR-Lex for the EU AI Act, MDR/IVDR, GDPR and EHDS; national statutes and official implementation guidance.
2. Standards and public-body guidance — e.g. NIST publications, HL7/FHIR specifications and WHO guidance. These are authoritative guidance, not peer-reviewed research.
3. Consensus and reporting guidance — e.g. FUTURE-AI, TRIPOD+AI, DECIDE-AI, CONSORT-AI and PROBAST+AI. Compliance with a guideline is not by itself proof of safety or effectiveness, but it signals that the evidence has been reported transparently.
4. Scholarly literature — especially independent validation, prospective or live-service evaluations, and implementation studies that report operational outcomes, not only model metrics. We prefer peer-reviewed work; where we cite preprints (e.g. arXiv), they are early-stage scholarly work that has not completed peer review.
5. Vendor technical documentation — used only for vendor-specific behaviour, specifications, API schemas or intended-use statements, not as independent evidence of clinical utility.
6. Synthetic teaching cases — clearly labelled fictional scenarios designed to practise judgement, not to represent real patients or real evidence.

Healthcare AI Learning uses an AI-assisted authoring workflow. Drafting and revision are grounded in the source hierarchy above, followed by separate checks with different purposes. No step below is described as certification, accreditation or human expert validation.

• AI-assisted authoring and internal content review — released lesson prose has undergone internal semantic review for factual errors, misleading statements, overclaims and source mismatches, and released graded quiz, case and lab items have been reviewed for ambiguity: whether the keyed answer is uniquely defensible, distractors are genuinely wrong, and explanations agree with the key. This is our own review of our own material, carried out in defined passes rather than as a guarantee that every sentence is re-examined after every edit.
• Cross-model AI review — separate AI model passes reviewed parts of the content and assessment ground truth. This adds a different model’s critique; it is not a qualified human clinical, regulatory, scientific or legal review, and it is not “expert validation.”
• Synthetic-user testing — simulated learner personas and journeys were used to look for findability, comprehension, trust and interaction problems. These were synthetic users, not human research participants, usability-study participants or domain experts.
• Browser and runtime testing — routes and interactive exercises are exercised in a real browser on desktop and mobile sizes, including progress persistence, data export/import handling and legacy redirects. External links cited in the material are resolved, distinguishing genuine dead links from sites that block automated access.
• Deterministic structural QA — automated integrity commands can be run at any time and are re-run as part of release QA, including for releases. They apply structural checks over the content: that every keyed assessment, case and lab item resolves to a real option or category and carries a stem and rationale, that catalogue release state matches what is actually rendered, that regulatory dates and version pins match the texts we teach from, and that source links are neither duplicated nor malformed. These are internal structural checks: they can tell you whether the material is internally consistent, not whether a statement is true.

Human domain-expert review: none is currently claimed. No named human clinical, regulatory, scientific or legal reviewer has been recorded in Healthcare AI Learning’s central content-status data. Human review will be claimed only when a reviewer, role, scope and date can be stated explicitly.

Current status checked 19 September 2026.

Earlier beta records used the phrases “external/blind audit”, “amber”, “green” and “delta verification”. In those records, external/blind meant a separate AI/model pass performed without relying on the authoring model’s conclusions; it did not mean a human expert audit. Amber and green were traffic-light labels for whether specified findings remained open, not ratings of clinical, scientific or legal validity. Delta verification meant a deterministic and cross-model re-check limited to changes made after an earlier findings list.

The final recorded re-check on 14 September 2026 found no remaining critical, high or medium items from that bounded list. That statement describes the review process and frozen material at that time. It is not independent human domain-expert sign-off and does not establish that the content is correct, compliant or future-proof.

Healthcare AI regulation and the underlying technical standards are moving quickly. We mark regulatory content with an “as of” date where it matters, and where a module teaches from a fast-moving technical specification, its reference list pins the exact version or revision used — for example specific protocol revisions, standard releases and dated framework editions. Pinning applies to those module reference lists, not to every glossary entry or visual guide. Regulatory and legal summaries were checked against the primary texts between 25 August and 10 September 2026.

Sources themselves can change after that date. Before you act on a legal or regulatory statement — especially conformity, classification or notification questions — verify the current text and any national transposition or guidance.

Key primary sources: EU AI Act, MDR, IVDR, GDPR and EHDS.

• The review process above reduces risk; it does not prove the absence of all errors, and it is not a certification, accreditation or independent validation of Healthcare AI Learning.
• The 14 September 2026 re-check found no remaining critical, high or medium items from its bounded findings list for that frozen release. It was a deterministic and cross-model AI review process, not human expert validation. It is not certification, not a regulatory determination and not evidence of compliance, and it does not guarantee that later edits, or changes in the underlying law and evidence, leave the material correct.
• Our deterministic checks are structural. A passing run does not prove that any teaching statement or keyed answer is semantically correct.
• Nothing here is medical advice for a specific patient, or legal advice for a specific product or organisation.

Regulatory texts, technical standards and the evidence base evolve, and this material is reviewed and version-pinned on an ongoing basis rather than treated as finished. If you believe something is factually wrong or out of date, treat the primary source as authoritative and flag the discrepancy so it can be corrected. Please use the contact page and identify the relevant page and source where possible.

Several exercises use fictional patients, hospitals, vendors and datasets. They are labelled as synthetic or illustrative and are designed to let you practise assessment and decision making without exposing real patient data or relying on real product claims. Do not treat any synthetic case as clinical guidance for an actual patient.

Every available module has its own collapsible Sources & evidence panel inside the lesson. That panel contains the exact references for that module, so you can verify claims in context without wading through a global bibliography.

• AI in Healthcare Essentials4 modules · open any module for its Sources & evidence panel
• AI in Healthcare10 modules · open any module for its Sources & evidence panel
• Agentic AI in Healthcare4 modules · open any module for its Sources & evidence panel
• EU Regulatory Readiness for Health AI Vendors7 modules · open any module for its Sources & evidence panel
• Building GenAI Applications in Healthcare6 modules · open any module for its Sources & evidence panel
• Healthcare Data & FHIR for AI1 available module · 6 planned · open the available module for its Sources & evidence panel
• Clinical Machine Learning with Python2 available modules · 10 planned · open the available modules for its Sources & evidence panel