AI is moving quickly into health technology assessment, but this episode focuses on the bigger question: is trust in the output keeping pace with the speed of adoption?

The discussion makes clear that in HTA, faster is only useful if the evidence remains robust, credible, and ready for scrutiny. The panel argues that AI should not be judged only by whether it saves time, but by whether it helps produce work that decision-makers can trust. That matters in a field where plausible is not enough and methodological rigor is everything.

A recurring theme is that AI can shift effort rather than remove it. Professor Rachael Hunter describes AI-assisted work that looked reasonable on the surface but still required detailed checking and correction. Jack Ishak makes a similar point in literature reviews: AI may help with abstract screening, but it is less reliable when extracting detailed evidence. Time saved early can easily be lost later in validation and quality control.

The episode also explores the risk of AI creating an illusion of competence. Professor Gianluca Baio argues that AI is most useful when it supports people who already understand the underlying methods. Used well, it can accelerate expert work. Used badly, it can allow people to move quickly without recognising when the output is flawed.

Transparency is another major theme. As AI becomes more common in HTA workflows, regulators and reviewers will likely expect clearer documentation of how it was used, what human oversight was applied, and how outputs were validated. The panel also considers who will shape the rules, with consultancies likely to move fastest, academia more cautiously, and HTA agencies remaining the ultimate gatekeepers.

Overall, the episode presents AI in HTA as promising but still unresolved. Adoption is already happening. The real challenge now is building the standards, governance, and skills needed to ensure that faster workflows still produce evidence that is rigorous, transparent, and trusted.