ISO/IEC TS 6254:2025 (Information technology — Artificial intelligence — Objectives and approaches for explainability and interpretability of machine learning (ML) models and artificial intelligence (AI) systems): https://www.iso.org/standard/82148.html

ISO/IEC CD TS 8200:2024 (Information technology — Artificial intelligence — Controllability of automated artificial intelligence systems): https://www.iso.org/standard/83012.html

ISO/IEC 22989:2022 (Information technology — Artificial intelligence — Artificial intelligence concepts and terminology): https://www.iso.org/standard/74296.html

ISO/IEC 23894:2023 (Information technology — Artificial intelligence — Guidance on risk management): https://www.iso.org/standard/77304.html

ISO/IEC TR 24028:2020 (Information technology — Artificial intelligence — Overview of trustworthiness in artificial intelligence): https://www.iso.org/standard/77608.html

ISO/IEC TR 24029-1:2021 (Artificial Intelligence (AI) — Assessment of the robustness of neural networks Part 1: Overview): https://www.iso.org/standard/77609.html

ISO/IEC 24029-2:2023 (Artificial Intelligence (AI) — Assessment of the robustness of neural networks Part 2: Methodology for the use of formal methods): https://www.iso.org/standard/79804.html

ISO/IEC 25012:2008 (Software engineering — Software product Quality Requirements and Evaluation (SQuaRE) — Data quality model): https://www.iso.org/standard/35736.html

ISO/IEC 27001:2022/Amd 1:2024 (Information security, cybersecurity and privacy protection — Information security management systems — Requirements): https://www.iso.org/standard/88435.html

ISO/IEC 27002:2022 (Information security, cybersecurity and privacy protection — Information security controls): https://www.iso.org/standard/75652.html

ISO/IEC 27006-1:2024 (Information security, cybersecurity and privacy protection — Requirements for bodies providing audit and certification of information security management systems): https://www.iso.org/standard/82908.html

ISO/IEC 27035-1:2023 (Information technology — Information security incident management): https://www.iso.org/standard/78973.html

ISO 42001:2023 (Information technology — Artificial intelligence — Management system): https://www.iso.org/standard/42001

ISPE AI GAMP: Artificial Intelligence GUIDE: https://ispe.org/publications/guidance-documents/gamp-guide-artificial-intelligence

WHO Annex 3 Good manufacturing practices: guidelines on validation: https://cdn.who.int/media/docs/default-source/medicines/norms-and-standards/guidelines/production/trs1019-annex3-gmp-validation.pdf?sfvrsn=9440a5c_0&download=true

ASME VV40: https://www.asme.org/codes-standards/find-codes-standards/assessing-credibility-of-computational-modeling-through-verification-and-validation-application-to-medical-devices

FDA Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological

FDA Data Integrity and Compliance With Drug CGMP: https://www.fda.gov/media/119267/download

FDA Assessing the Credibility of Computational Modeling and Simulation in Medical Device Submissions - Guidance for Industry and Food and Drug Administration Staff: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/assessing-credibility-computational-modeling-and-simulation-medical-device-submissions

FDA Guidance for Industry Process Validation: General Principles and Practices: https://www.fda.gov/files/drugs/published/Process-Validation--General-Principles-and-Practices.pdf

FDA PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/pat-framework-innovative-pharmaceutical-development-manufacturing-and-quality-assurance

FDA-MHRA-Health Canada Transparency for Machine Learning-Enabled Medical Devices: Guiding Principles: https://www.fda.gov/medical-devices/software-medical-device-samd/transparency-machine-learning-enabled-medical-devices-guiding-principles

EMA and FDA set common principles for AI in medicine development: https://www.ema.europa.eu/en/news/ema-fda-set-common-principles-ai-medicine-development-0

EMA Data quality framework for medicines regulation: https://www.ema.europa.eu/en/about-us/how-we-work/data-regulation-big-data-other-sources/data-quality-framework-medicines-regulation

EMA Data Quality Framework for EU medicines regulation: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/data-quality-framework-eu-medicines-regulation_en.pdf

EMA Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle: https://www.ema.europa.eu/system/files/documents/scientific-guideline/reflection-paper-use-artificial-intelligence-ai-medicinal-product-lifecycle-en.pdf

MHRA Impact of Artificial Intelligence on the Regulation of Medical Products: https://assets.publishing.service.gov.uk/media/662fce1e9e82181baa98a988/MHRA_Impact-of-AI-on-the-regulation-of-medical-products.pdf

Validation & Uncertainty Quantification

Safety, security & robustness

Data & digital tools:

Ensure data is representative of and practical for application within CoU and robust (accurate and reproducible); model calibration and bench-testing performed; Cross-laboratory validation supported; transparent reporting; reproducibility and credibility demonstrated.

Personnel Training:

Use of industry relevant examples to assess robustness and credibility.

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