Concept & Risk Analysis

Safety, security & robustness

Data & digital tools: 

Data sources defined; validity, accuracy, and precision considered; lifecycle and risk assessment initiated. Rationale and methodology for data capture defined; context of use clearly articulated.​

Personnel Training:

Appropriate cybersecurity awareness and training.​

Links:

ICH Q8(R2): https://database.ich.org/sites/default/files/Q8%28R2%29%20Guideline.pdf

ICH Q10: https://database.ich.org/sites/default/files/Q10%20Guideline.pdf

ICH Q12: https://database.ich.org/sites/default/files/Q12_Guideline_Step4_2019_1119.pdf

ICH M15: https://database.ich.org/sites/default/files/ICH_M15_EWG_Step2_DraftGuideline_2024_1031.pdf

ISO/IEC 5259:2024-2025 (AI data quality management bundle): https://www.iso.org/publication/PUB200525.html

ISO/IEC 5469 :2024 (Functional safety and AI systems): https://www.iso.org/standard/81283.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 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 Data Quality Assurance Module 4 Data Quality Review: https://cdn.who.int/media/docs/default-source/world-health-data-platform/data-quality-assurance/2023_who_dqr_community_module4_working-document_april2023.pdf

WHO Data Principles: https://www.who.int/docs/default-source/world-health-data-platform/who-data-principles-10aug-%283%29.pdf

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 Good Machine Learning Practice for Medical Device Development: Guiding Principles: https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-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 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

UK Government Implementing the UK’s AI regulatory principles: initial guidance for regulators: https://www.gov.uk/government/publications/implementing-the-uks-ai-regulatory-principles-initial-guidance-for-regulators