AI Decision Support in Life Sciences

The Challenge

Life sciences organisations manage vast volumes of scientific literature, internal research documents, regulatory guidance, and experimental data. Researchers and regulatory teams faced significant time pressure when synthesising evidence, responding to queries, or preparing submissions.

Key challenges included fragmented knowledge sources, inconsistent documentation practices, and high risk associated with misinterpretation or hallucinated outputs from generic AI tools.

The Approach

The team worked closely with scientific, regulatory, and IT stakeholders to design a controlled AI system focused on decision support — not automation of scientific judgement.

The design prioritised traceability, citation-backed outputs, and clear boundaries on where AI assistance could and could not be applied.

The Solution

The resulting platform enables researchers to query approved document repositories using natural language, rapidly surface relevant evidence, and generate structured summaries with direct source attribution.

• Scientific literature and internal report ingestion
• Vector-based retrieval across approved sources
• Evidence-backed summarisation and comparison
• Human-in-the-loop review for all outputs

Governance & Risk Controls

• Restricted ingestion to validated and approved sources
• Mandatory citation display for all generated content
• Clear usage guidance embedded in the interface
• Audit logging for regulatory review

Measured Impact

• 30–50% reduction in time spent on literature synthesis
• Improved consistency in regulatory documentation
• Higher confidence in AI-assisted outputs

Why This Matters

This case demonstrates how AI can be safely and effectively applied in highly regulated environments when governance, traceability, and domain expertise are embedded from the outset.

It illustrates a practical pathway for life sciences organisations to benefit from AI without compromising scientific integrity or compliance.