NICE has recently completed a series of stakeholder workshops exploring the potential applications, opportunities, and challenges of Generative AI (GenAI) in health economic evaluation (HEE). These discussions build on NICE’s 2024 Position Statement on the Use of AI in Evidence Generation, which acknowledges that AI methods are likely to play an increasing role in future HTA submissions — provided they are used transparently, responsibly, and give clear added value.

In this article, we explore why so many AI‑powered diagnostics and decision‑support tools may stall after regulatory clearance. By tracing the feedback loop between evolving rules, payment incentives, and clinician‑patient confidence, we discuss how each barrier reinforces the next. Despite hurdles, a practical route forward does exist for AI medical devices: harmonised guidance, value‑based financing, and design principles that make algorithms transparent may all facilitate the latest AI innovations in travelling from code to care.

The healthcare landscape is undergoing a profound transformation with the integration of artificial intelligence (AI) meaning that health technology assessment (HTA) stands at a critical crossroads. While a previous discussion examined how generative AI is reshaping traditional HTA processes, a more sophisticated and promising development is emerging: agentic deep research. This approach represents a fundamental reimagining of how evidence is generated, synthesised, and applied to inform healthcare decision-making.