The benchmark gap
Generic accuracy scores rarely reveal whether a system selected the right jurisdiction, recognised a pivotal contradiction, used current evidence, expressed appropriate uncertainty, or escalated a decision that required a human owner. Those are precisely the behaviours that determine value in drug development, and they are exactly what a headline benchmark score tends to hide.
Five layers of a credible evaluation
- Context: intended user, workflow, source set and decision consequence
- Task design: representative work with explicit boundaries and rights-cleared materials
- Rubric: accuracy, relevance, traceability, uncertainty, consistency and escalation
- Adjudication: multiple qualified reviewers and a process for legitimate expert disagreement
- Operational control: human review, monitoring, change management and residual-risk ownership
Why 2025 raised the stakes
The FDA's Center for Drug Evaluation and Research approved 46 novel drugs in 2025 — combined with CBER, 58 novel approvals overall, more than half of them for rare-disease indications. As agencies openly discuss using AI internally to speed review, and sponsors look to AI to accelerate their own submissions, the cost of an unevaluated failure rises: a missed contradiction or an overconfident answer inside a regulatory workflow can move a programme timeline by months, not minutes.
The commercial opportunity
AI builders need credible evidence for product design and customer adoption. R&D teams need a controlled way to select and implement tools. A domain-led evaluation layer can serve both — while creating reusable task structures and failure taxonomies that improve with every authorised project.