AI in Life Sciences: from experimentation to controlled use.
A practical introduction to AI, Generative AI and AI-enabled systems—and the questions a regulated organization should ask before using them in a GxP process.
AI concepts you should understand
Artificial Intelligence
Technology that performs tasks commonly associated with human intelligence, such as classification, prediction, language processing or decision support.
Machine Learning
Models learn patterns from data and use those patterns to make predictions or classifications.
Generative AI
AI that generates content such as text, images, code or summaries. Outputs require appropriate controls when used in regulated work.
Large Language Models
Models trained on large amounts of text to predict and generate language. They can be useful but may produce inaccurate or unsupported outputs.
RAG
Retrieval-Augmented Generation connects a language model with selected information sources to improve grounding and traceability.
AI Agents
Systems that can plan or execute multi-step actions. The greater the autonomy, the more important controls, permissions and monitoring become.
Does AI change the assurance conversation?
Yes. AI-enabled systems can introduce additional considerations beyond conventional deterministic software. The assurance strategy should be based on intended use and risk—not simply on the fact that a product uses AI.
Intended use
Define exactly what the AI is permitted to do, who uses the output and what decisions it supports.
Data
Understand data sources, quality, provenance, privacy, access and whether data changes over time.
Performance
Define appropriate evaluation criteria, acceptance thresholds and monitoring methods.
Human oversight
Determine when a qualified person must review, approve, correct or reject an AI output.
Change
Consider model updates, vendor changes, prompts, configurations, data changes and retraining.
Transparency
Maintain enough information to understand intended use, limitations, decisions, evidence and accountability.
A practical risk-based lifecycle
Use Case
Business need
GxP Impact
Regulated process?
Risk
What can go wrong?
Data
Quality & privacy
Evaluate
Test performance
Assure
Evidence & controls
Approve
Business/QA decision
Monitor
Ongoing control
Reassess
Change & retirement
Where AI may support Life Sciences teams
Quality
Document classification, trend analysis, knowledge search and decision support with human review.
Regulatory
Literature or regulatory information summarization, drafting support and controlled knowledge retrieval.
Pharmacovigilance
Potential support for case intake, classification and prioritization, subject to appropriate controls and review.
Clinical
Data and document support, protocol-related analysis and knowledge assistance with process-specific oversight.
Manufacturing
Analytics, anomaly detection and decision support where data integrity and process controls are maintained.
Knowledge Management
Enterprise assistants and RAG-based search can help users find approved information more efficiently.
Have an AI-enabled system or AI use case to assess?
ILAP Advisory can help structure an initial discussion around intended use, GxP impact, risk, human oversight, assurance evidence and lifecycle controls. You are also welcome to use this page purely as an educational resource.
Discuss an AI / GxP RequirementIndustry reference: ISPE's GAMP® Good Practice Guide for computerized GCP systems and data discusses AI-enabled systems, human oversight, bias, transparency and human-machine interaction.