跨受监管医疗行业中含人工智能产品的验证
Validation of artificial intelligence containing products across the regulated healthcare industries
AI总结：
该研究通过比较方法统一跨受监管医疗行业（制药、医疗器械、体外诊断）中AI/ML软件验证的术语和方法，提出广义与狭义验证区分，以促进跨部门协作和流程优化。
摘要：
目的：人工智能/机器学习（AI/ML）产品引入到制药研发（R&D）和药物制造、医疗器械（MD）和体外诊断（IVD）等受监管领域，带来了新的监管问题：缺乏通用术语和理解导致混乱、延误和产品失败。验证作为产品开发中的关键步骤，在包括计算机系统和AI/ML开发的每个领域中都是共同的，为跨部门产品开发中协调人员和流程提供了合适的比较点。方法：采用基于研讨会和后续书面交流序列的比较方法，总结为适合混合团队工作的查找表。结果：1. 一种自下而上、以定义为主导的方法，区分了广义与狭义验证及其与监管制度的关系。2. 对含AI软件验证的主要方法进行共同基础介绍。3. 制药药物开发和MD/IVD关于合规AI软件开发的特定视角，作为合作的基础。结论：在人类健康受监管行业中，统一含人工智能/机器学习（AI/ML）组件的软件产品验证所用的术语和方法，是简化流程和改善工作流程的关键第一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
