Artificial intelligence in key pricing, reimbursement, and market access (PRMA) processes: better, faster,...

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.