AI · Pharmaceutical Industry Practical Discussion
Where can AI be applied in the pharmaceutical lifecycle?
Realistic AI use cases by lifecycle stage — and where caution is needed.
- Concept
- Practical example
- Process
- Documentation
- Technology
- Quality considerations
- Example use case
01Concept
AI is most useful where people handle large volumes of text or data: literature, documents, deviations, laboratory trends, process data. It is least appropriate where decisions need certainty it cannot provide.
02Practical example
A retrieval-based assistant answers questions from approved SOPs and cites the source; the SOP remains the authority.
03Process
- 1
Find a painful, data-rich task
- 2
Check data availability and quality
- 3
Assess risk and intended use
- 4
Design human oversight
- 5
Build and test
- 6
Validate
- 7
Monitor
04Documentation
- Intended use statement
- Risk assessment
- Data assessment
- Validation report
- Monitoring plan
05Technology
Large language models, retrieval-augmented generation, machine learning and statistical analysis.
06Quality considerations
GxP-relevant AI is treated as a computerised system with added controls for data, model performance and change.
07Example use case
Deviation trending uses similarity search to link new events to past ones for the investigator.
Example use cases are illustrative. This article is general educational information, not regulatory or legal advice.
