INSIGHT / AI ASSURANCE
Drug-development AI needs expert evaluation
A polished answer is not evidence that an AI system can operate safely or usefully in regulated R&D. Evaluation must begin with the real context, consequence and failure modes of the work.
VED working method
- 01Context
- 02Failure
- 03Control
The benchmark gap
Generic accuracy scores do not reveal whether a system selected the right jurisdiction, recognised a pivotal contradiction, used current evidence, expressed uncertainty or escalated a decision that required a human owner. Those are precisely the behaviours that determine value in drug development.
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
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.
Start with the constraint
What must your programme decide, prove or deliver next?
Share the situation, evidence and timing. We will propose the smallest credible first step.