Randomized controlled trials (RCTs) have been the cornerstone of clinical evidence; however, their cost, duration, and restrictive eligibility criteria limit power and external validity. Studies using real-world data (RWD), historically considered less reliable for establishing causality, are now recognized to be important for generating real-world evidence (RWE). In parallel, artificial intelligence and machine learning (AI/ML) are being increasingly used throughout the drug development process, providing scalability and flexibility but also presenting challenges in interpretability and rigor that traditional statistics do not face. This Perspective argues that the future of evidence generation will not depend on RCTs versus RWD, or statistics versus AI/ML, but on their principled integration. To this end, a causal roadmap is needed to clarify inferential goals, make assumptions explicit, and ensure transparency about tradeoffs. We highlight key objectives of integrative evidence synthesis, including transporting RCT results to broader populations, embedding AI-assisted analyses within RCTs, designing hybrid controlled trials, and extending short-term RCTs with long-term RWD. We also outline future directions in privacy-preserving analytics, uncertainty quantification, and small-sample methods. By uniting statistical rigor with AI/ML innovation, integrative approaches can produce robust, transparent, and policy-relevant evidence, making them a key component of modern regulatory science.

翻译：随机对照试验（RCTs）长期以来是临床证据的基石，但其成本高昂、周期漫长且纳入标准严格，限制了统计效能与外推有效性。基于真实世界数据（RWD）的研究虽历史上被认为在因果推断方面可靠性不足，现已被广泛认可为生成真实世界证据（RWE）的重要途径。与此同时，人工智能与机器学习（AI/ML）在药物研发全流程中的应用日益增多，虽提供了可扩展性与灵活性，但也带来了传统统计学未面临的解释性与严谨性挑战。本文主张，未来证据生成的发展方向不应是RCT与RWD的对立，或统计学与AI/ML的取舍，而应致力于二者的原则性整合。为此，需要构建因果推断路线图以明确推论目标、使假设显性化，并确保权衡取舍的透明度。我们重点阐述了整合证据综合的关键目标，包括：将RCT结果外推至更广泛人群、在RCT中嵌入AI辅助分析、设计混合对照试验，以及利用长期RWD延伸短期RCT的观察维度。同时，我们展望了隐私保护分析、不确定性量化与小样本方法等未来研究方向。通过融合统计严谨性与AI/ML创新，整合性方法能够生成稳健、透明且具政策相关性的证据，使其成为现代监管科学的核心组成部分。