Purpose: The introduction of artificial intelligence / machine learning (AI/ML) products to the regulated fields of pharmaceutical research and development (R&D) and drug manufacture, and medical devices (MD) and in-vitro diagnostics (IVD), poses new regulatory problems: a lack of a common terminology and understanding leads to confusion, delays and product failures. Validation as a key step in product development, common to each of these sectors including computerized systems and AI/ML development, offers an opportune point of comparison for aligning people and processes for cross-sectoral product development. Methods: A comparative approach, built upon workshops and a subsequent written sequence of exchanges, summarized in a look-up table suitable for mixed-teams work. Results: 1. A bottom-up, definitions led, approach which leads to a distinction between broad vs narrow validation, and their relationship to regulatory regimes. 2. Common basis introduction to the primary methodologies for AI-containing software validation. 3. Pharmaceutical drug development and MD/IVD specific perspectives on compliant AI software development, as a basis for collaboration. Conclusions: Alignment of the terms and methodologies used in validation of software products containing artificial intelligence / machine learning (AI/ML) components across the regulated industries of human health is a vital first step in streamlining processes and improving workflows.

翻译：目的：人工智能/机器学习(AI/ML)产品在制药研发与生产、医疗器械及体外诊断设备等受监管领域的引入带来新的监管问题：术语不统一和理解差异导致界面模糊、项目延误及产品失败。验证作为产品开发的关键环节，在计算机化系统和AI/ML开发中均属共性流程，为跨领域产品开发中的人员协调与流程对接提供了理想比较基础。方法：通过研讨会及后续书面交流序列构建比较框架，以适合混合团队工作的对照表形式呈现成果。结果：1. 自下而上的定义驱动方法，区分广义验证与狭义验证及其与监管体系的关系；2. 建立含AI软件验证主要方法的共同基础；3. 基于合规要求的制药研发与医疗器械/体外诊断设备特异性视角，搭建协作基础。结论：在人类健康领域的受监管行业中，统一含人工智能/机器学习组件软件产品的验证术语和方法，是优化流程与改善工作流的关键第一步。