AI for Clinical Research & Regulatory Affairs
Find and extract evidence across large study sets and submission dossiers, with citations bound to the claims that use them and a reviewer deciding what counts.
Built for
Why Healthcare teams struggle today
The problems we see in every healthcare workflow.
- Regulatory submissions to FDA/EMA require meticulous documentation across thousands of pages
- Adverse event reports buried across multiple systems delay safety signals
- Clinical trial data extraction is manual and inconsistent across studies
- Systematic reviews take 6-12 months of researcher time
- Evidence for a submission is scattered across protocols, reports and correspondence
How DataStruct AI solves it
Purpose-built capabilities for healthcare workflows.
- Clinical trial data extraction (endpoints, sample sizes, outcomes, adverse events)
- Adverse event extraction across studies, linked to source
- Rule packs you define for ICH, FDA and EMA submission requirements
- Cross-study evidence synthesis with source-grounded summaries
- Source-linked extraction from published literature
Healthcare use cases
The most impactful workflows DataStruct AI powers for healthcare teams.
Systematic Reviews
Problem: Cochrane-grade systematic reviews take 6-12 months and require teams of researchers.
Solution: Extract study data across the paper set with source links; screening and synthesis decisions stay with the review team.
Impact: Faster reviews with reproducible methodology and an audit log on reviewer decisions.
Regulatory Submissions
Problem: FDA and EMA submissions require precise cross-referencing across thousands of pages.
Solution: Rule packs you define for required sections, with source-linked extraction across the dossier for reviewer checking.
Impact: Gaps surfaced for the regulatory team before submission.
Adverse Event Detection
Problem: Safety signals hide in narrative text across thousands of case reports.
Solution: Extraction of reported adverse events with source links; severity is assigned by rules you define.
Impact: Reported events collected in one source-linked register for review.
Literature Data Extraction
Problem: Pulling study design, populations and outcomes from hundreds of papers by hand is slow and inconsistent.
Solution: Define the fields once and extract them across the paper set, each value linked to its source passage.
Impact: Structured, source-linked study data ready for reviewer checking.
Document types we handle
Questions you can ask
- ›“What was the primary endpoint result?”
- ›“Compare efficacy across studies in this evidence set”
- ›“What adverse events were reported in Phase III?”
- ›“Summarise safety signals across all trials”
- ›“Which inclusion/exclusion criteria are most common?”
Why Healthcare teams choose DataStruct AI
Evidence-cited AI
Answers cite the sources they rely on, and records the system could not confirm are shown separately for a reviewer to check.
Access control and audit
Workspace roles with granular permissions and an audit log on sensitive actions. Tenant isolation is enforced in the application layer.
11 industries
Pre-configured terminology, document types, and rule packs for your vertical.
See DataStruct AI in action
Tell us about your team, document volumes, and integrations. We'll put together a tailored package and walk you through the platform.