Applications of artificial intelligence (AI) in drug development continue to increase at a rapid pace. Regulatory authorities have provided increasingly clear perspectives on the use of AI in regulated applications, including recent draft guidance from FDA that provides a 7-step risk-based framework to assess AI model credibility for these cases. We present an application of AI models to prospectively reduce the planned sample size in a randomized controlled trial, using model-derived prognostic covariates. This can shorten trial timelines, enable faster decision making, and lower costs. When treatments are effective and tolerable they can be accessible to patients sooner, which is a compelling use case for the FDA guidance. We walk through each of the steps in the guidance, providing general recommendations for model development, evaluation, and approaches for sample size determination, with the intent of providing a clear set of guidelines on how to engage with the FDA guidance and advance responsible use of AI in drug development. We demonstrate the application with an example in Alzheimerś Disease.

翻译：人工智能在药物开发中的应用正在迅速增长。监管机构对人工智能在受监管应用中的使用提供了日益清晰的视角，包括FDA近期发布的指南草案，其中提出了一个基于风险的七步框架，用于评估这些情况下人工智能模型的可信度。我们提出了一种应用人工智能模型的前瞻性方法，通过使用模型推导的预后协变量，减少随机对照试验的计划样本量。这可以缩短试验时间，加快决策速度并降低成本。当治疗有效且耐受性良好时，患者可以更早获得治疗，这是FDA指南中极具说服力的应用案例。我们逐一解析指南中的每个步骤，提供模型开发、评估及样本量确定方法的通用建议，旨在建立一套清晰的指导原则，说明如何遵循FDA指南，并推动人工智能在药物开发中的负责任应用。我们以阿尔茨海默病为例展示了该应用。