Development & Verification

Appropriate transparency & explainability

Data & digital tools: 

Data and model rationale and methodology shared and aligned with experimentalists. Model development measures to enhance interpretability and mitigate model biases may also be implemented and documented.

Personnel Training:

Sandbox environments used to train users. Logic and process used collected for user training development. Develop mechanisms to provide clear explanations of model generated decisions.​

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 Q13: https://database.ich.org/sites/default/files/ICH_Q13_Step4_Guideline_2022_1116.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 12792:2025 (Information technology — Artificial intelligence (AI) — Transparency taxonomy of AI systems): https://www.iso.org/standard/84111.html

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

WHO Ethics and Governance for AI for Health: https://www.who.int/publications/i/item/9789240084759

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 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

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

FDA Assessing the Credibility of Computational Modeling and Simulation in Medical Device Submission: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/assessing-credibility-computational-modeling-and-simulation-medical-device-submissions

FDA Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/artificial-intelligence-enabled-device-software-functions-lifecycle-management-and-marketing

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

UK Gov Transparency for machine learning-enabled medical devices: guiding principles: https://www.gov.uk/government/publications/machine-learning-medical-devices-transparency-principles/transparency-for-machine-learning-enabled-medical-devices-guiding-principles