Dietrich Eva Susanne
Institute for Evidence-based Positioning in the Healthcare Sector, Bonn, Germany.
J Med Econ. 2025 Dec;28(1):586-595. doi: 10.1080/13696998.2025.2488154. Epub 2025 Apr 17.
The rapid evolution of large language models (LLMs) and machine learning (ML) presents both significant opportunities and challenges for market access processes. These sophisticated AI systems, built on transformer architectures and extensive datasets, offer potential to forecast claims and decisions of health technology assessment (HTA) agencies and streamline processes, such as systematic literature reviews and HTA submissions. Furthermore, the analysis of real-world data-also for deriving causal relationships-is being discussed intensively. Despite notable advancements, their adoption in key PRMA processes is still limited at present, with only a small fraction of submissions to HTA bodies incorporating AI. Key barriers include stringent transparency requirements, the necessity of explainability and human oversight in data analyses, and the highly sensitive nature of text drafting-especially in cases where reimbursement decisions or pricing negotiations balance on a knife's edge. These requirements are often not met due to the immaturity of many AI applications, which still lack the necessary precision, reliability, and contextual understanding. Moreover, AI-generated evidence has yet to prove its validity before it can supplement or replace traditional study designs, such as randomized controlled trials (RCTs), which are critical for HTA decisions. Additionally, the environmental and financial costs of training LLMs require careful assessment. This paper explores various current AI applications, their limitations, and future prospects in key PRMA processes from a German perspective while also considering the broader implications of the EU Health Technology Assessment Regulation (HTAR). It concludes that while AI hold transformative potential, its integration into workflows must be approached cautiously, with incremental adoption, and close collaboration between industry, HTA agencies, and academia. Demonstrating robust, unbiased comparative evidence-showcasing superior performance and cost savings over traditional methods-could accelerate the adoption process.
大语言模型（LLMs）和机器学习（ML）的快速发展给市场准入流程带来了重大机遇和挑战。这些基于变压器架构和大量数据集构建的先进人工智能系统，有望预测卫生技术评估（HTA）机构的索赔和决策，并简化流程，如系统文献综述和HTA提交。此外，对真实世界数据的分析——也是为了得出因果关系——正在被深入讨论。尽管取得了显著进展，但目前它们在关键的药品风险管理评估（PRMA）流程中的应用仍然有限，向HTA机构提交的材料中只有一小部分纳入了人工智能。主要障碍包括严格的透明度要求、数据分析中可解释性和人工监督的必要性，以及文本起草的高度敏感性——特别是在报销决策或定价谈判处于微妙平衡的情况下。由于许多人工智能应用的不成熟，这些要求往往无法满足，它们仍然缺乏必要的精度、可靠性和情境理解。此外，在人工智能生成的证据能够补充或取代传统研究设计（如随机对照试验（RCT），这对HTA决策至关重要）之前，还需要证明其有效性。此外，训练大语言模型的环境和财务成本需要仔细评估。本文从德国的角度探讨了当前人工智能在关键PRMA流程中的各种应用、它们的局限性以及未来前景，同时也考虑了欧盟卫生技术评估法规（HTAR）的更广泛影响。结论是，虽然人工智能具有变革潜力，但其融入工作流程必须谨慎进行，逐步采用，并促进行业、HTA机构和学术界之间的密切合作。展示强大、无偏的比较证据——展示优于传统方法的性能和成本节约——可以加速采用过程。
