EU AI Policy Observatory

Published document record

EMA/FDA good AI practice in drug development

PDF source copies

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  • Official PDF — unmodified source copy

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    Correct joint EMA/FDA January2026 guiding principles. Both pages visually inspected: introduction and all ten principles. Not a longer guidance report or an abridged substitute.

    Source: European Medicines Agency, U.S. Food and Drug Administration. Retrieved 2026-09-10.

    Copy provenance and reuse

    EMA legal notice permits complete document reproduction/distribution with EMA acknowledgement unless otherwise stated, and defaults document ownership to EMA. No third-party credit or contrary notice found in either page; retain embedded joint EMA/FDA identity and credit both issuers, without claiming endorsement or separately reusing logos. Source reuse terms. The database metadata licence does not replace these terms.

    SHA-256: 08880e6fe51a0f17ba40168ea21953e5a4b600d97d66e2193000cbc87ab75f99

Official metadata

Official title
Guiding principles of good AI practice in drug development
Document type
Guidelines
Document date
2026-01-14
Document date kind
Publication
Document-date evidence
European Medicines Agency · EMA AI catalogue, Principles for AI in medicine development, matching English document: First published 14/01/2026. — Exact official publication used because no precise issue day is evidenced.
Publication date
2026-01-14
Publication-date evidence
European Medicines Agency · Principles for AI in medicine development: matching English PDF, First published 14/01/2026. — Official release evidence for this independently citable text.
Additional official dates
Version label
January 2026 guiding principles
Legal status
Non binding
Legal-status evidence
European Medicines Agency · Scope and principles; recommendations for good practice. — Institutional research or high-level guidance, not a binding legal instrument.
Language
en

Research classifications

These are researcher classifications used to organise and analyse the corpus; they are not official EU metadata.

Record level
Principal
Version status
Final
Temporal collection
Contemporary EU AI policy
Relevance class
Direct AI relevance
Policy stage
Implementation
Corpus tier
Directly related
Sectors
  • Health
  • Research and innovation
  • Industry and manufacturing
Policy membership
  • EU AI policy pathway

Classification evidence

  • Relevance class: Direct AI relevance · European Medicines Agency · PDF pp.1-2: evidence generation throughout nonclinical, clinical, post-marketing and manufacturing phases; ten AI governance, validation and monitoring principles. — AI is a substantive policy or sectoral subject throughout the cited passages.
  • Sector tags: Health · European Medicines Agency · PDF pp.1-2: evidence generation throughout nonclinical, clinical, post-marketing and manufacturing phases; ten AI governance, validation and monitoring principles. — Positive researcher classification based on the stated substantive scope.
  • Sector tags: Research and innovation · European Medicines Agency · PDF pp.1-2: evidence generation throughout nonclinical, clinical, post-marketing and manufacturing phases; ten AI governance, validation and monitoring principles. — Positive researcher classification based on the stated substantive scope.
  • Sector tags: Industry and manufacturing · European Medicines Agency · PDF pp.1-2: evidence generation throughout nonclinical, clinical, post-marketing and manufacturing phases; ten AI governance, validation and monitoring principles. — Positive researcher classification based on the stated substantive scope.
  • Provenance tags: Authored by an EU agency or body · European Medicines Agency · Principles for AI in medicine development, EMA/FDA joint production statement. — Provenance reflects evidenced production or official publication; hosting is not itself authorship.
  • Provenance tags: Officially published · European Medicines Agency · Principles for AI in medicine development: matching English PDF, First published 14/01/2026. — Provenance reflects evidenced production or official publication; hosting is not itself authorship.

Production provenance

Provenance tags
  • Authored by an EU agency or body
  • Officially published
Named bibliographic authors
Institutions and roles

Official sources and identifiers

Research assessment

Inclusion rationale
PDF pp.1-2: evidence generation throughout nonclinical, clinical, post-marketing and manufacturing phases; ten AI governance, validation and monitoring principles.
Researcher notes
Cover issue precision is January 2026 only; exact EMA publication supplies the date fallback. Joint EMA/FDA production is recorded in institutional and bibliographic authorship. The joint_institutional tag is not used because its published vocabulary requires more than one EU institution/body. FDA is a non-EU co-author, not an EU institution or external commissioned author. This review inspected the EMA manifestation and does not claim byte equivalence with the FDA PDF. The initial review left concept coding and policy-group assignment incomplete. Broad policy-group membership was recorded on 12 September 2026; concept-review history remains separate. This editorial grouping does not establish a legal or procedural relationship.
Review status
verified

Relationships

No additional published relationships touch this record.

Verification

Expanded evidence review
Verified
Reviewed by
Yichen Hao
Evidence review date
2026-09-06T22:52:47Z

The evidence review date is not a separate human sign-off date. Review credit and evidence-review actions

  • Last verified: 2026-09-06T22:52:47Z · AI-assisted evidence review. Fresh HTTP 200 capture, 580822 bytes, SHA-256 08880e6fe51a0f17ba40168ea21953e5a4b600d97d66e2193000cbc87ab75f99. PDF pp.1-2: evidence generation throughout nonclinical, clinical, post-marketing and manufacturing phases; ten AI governance, validation and monitoring principles. Capture retained outside canonical data; no public archive guarantee. Exact receipt in candidate-health-finance-20260906 audit. PDF extraction recovered with pypdf after optional fitz module was unavailable.
  • Last verified: 2026-09-06T22:52:47Z · AI-assisted evidence review. Fresh HTTP 200 capture, 104048 bytes, SHA-256 e7c169f99f4e0c5373c58a89373c35ea1f8e31716fdd7be16ec7bdfb752141d1. Principles for AI in medicine development: matching English PDF, First published 14/01/2026. Capture retained outside canonical data; no public archive guarantee. Exact receipt in candidate-health-finance-20260906 audit. PDF extraction recovered with pypdf after optional fitz module was unavailable.

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