AI in Clinical Trials – Efficient, Validated, and Under Human Control
Across the Flex Databases platform, AI-assisted workflows reduce repetitive work – filing documents, proposing medical codes, surfacing safety signals and drafting quiz questions – while your team retains judgement and accountability.
~80%
of TMF docs filed automatically after tuning
~6,600 h
potential hours saved per year at a large sponsor
GAMP 5
& FDA CSA-aligned validation
100%
human accountability retained
What AI takes off your plate
Someone opens each document, picks the folder, types the metadataAI files it and fills metadata — you confirm with one click
Adverse-event terms coded by hand against MedDRA and WHODrugAI proposes the code — your coder reviews and approves
Compliance staff writing quiz questions from an SOP by handAI drafts the assessment — you edit before it publishes
Our Approach to AI
Three commitments that decide what we build — and what we won't.
Useful by design
We add AI where it reduces measurable, repetitive work – filing, medical coding, drafting and screening. If it does not save time or improve consistency, it does not go.
Validated for GxP workflows
Each AI-assisted feature operates within a validated process, with audit trail, traceability and validation documentation available for inspection – the same discipline applied across the platform.
Human accountability stays in place
AI supports the workflow; authorized users remain accountable. Outputs can be reviewed, challenged and overridden, with controls appropriate to each use case.
Where AI Works Across the Platform
Four places today — each inside the module where the work already happens, on one data model.
AI TMF filing & classification
Send a document via email, drag-and-drop or phone photo, and AI proposes the filing location and metadata. Around 65% of documents can be classified and filed automatically out of the box, rising to 80%+ after tuning. Results vary by document mix, metadata set and configuration; exceptions are routed for review.
See it in eTMFAI-assisted medical coding
In EDC, AI proposes MedDRA and WHODrug terms for adverse events and medications. Your medical team reviews and approves them – reducing manual entry and supporting more consistent coding with the dictionaries your team already trusts.
See it in EDCML-powered signal detection
In Pharmacovigilance, disproportionality methods – MGPS and EBGM – together with machine-learning approaches help surface product–event combinations for review in dashboards, rather than relying only on periodic retrospective analysis.
See it in PVAI quiz generation
In LMS & QMS, AI turns your training materials into a draft knowledge assessment in a few clicks — based entirely on your content, with multiple question types and full editing before publication.Your compliance team wasn’t hired to write quiz questions. In LMS & QMS, AI builds a complete knowledge assessment from your training materials in a few clicks — based entirely on your content, multiple question types, fully editable before you publish.
See it in LMS & QMSEstimated TMF Filing Savings
Filing one document manually takes about 4–5 minutes – roughly €3.3 of someone’s time at a typical EU rate. AI can save times. Across a year, it adds up fast. These figures assume 80% automation after tuning.
| Client profile | Documents / year | Hours freed / year | Cost saved / year | FTE freed |
|---|---|---|---|---|
| Sponsor (large portfolio) | 100,000 | ~6,600 h | ~€267,000 | ~3.5 |
| CRO (large) | 60,000 | ~4,000 h | ~€160,000 | ~2.1 |
| Biotech (small) | 10,000 | ~670 h | ~€26,700 | ~0.35 |
Client profile
Sponsor (large portfolio)
Documents / year
100,000
Hours freed / year
~6,600 h
Cost saved / year
~€267,000
FTE freed
~3.5
Client profile
CRO (large)
Documents / year
60,000
Hours freed / year
~4,000 h
Cost saved / year
~€160,000
FTE freed
~2.1
Client profile
Biotech (small)
Documents / year
10,000
Hours freed / year
~670 h
Cost saved / year
~€26,700
FTE freed
~0.35
Based on ~5 minutes per document at an in-house EU rate of €40/hour, at 80% automation after tuning. FTE freed = hours freed ÷ ~1,900 productive hours per year, for the filing task specifically. Figures assume each organization maintains its own TMF. AI never reaches 100% — the remaining documents (bad scans, wet-ink originals, edge cases) still get human eyes, which is exactly where they belong.
Estimate your own savingsSee the Basis for Every Suggestion
Every suggestion is accompanied by information your team can review and challenge.
Reasoning you can read
Each AI suggestion includes the evidence used to support it. To decide a document’s country, the system points at concrete clues – signing timestamps in US Eastern time, a sponsor representative named as president of a US-headquartered company, context pointing to the US. You read a short chain of reasoning you can check and challenge, not a verdict from a black box.
Uncertain cases are routed for review
Some documents will not classify automatically: bad scans, handwritten signatures, wet-ink markings or records with too little context. Those cases are routed to a human reviewer by design.
Validation Designed for GxP Use
The relevant question is whether the AI-assisted process remains controlled, traceable and inspection-ready.
We validate the process, not just the model
Validation covers the whole business process around the AI – how its decisions affect data integrity, document completeness and regulatory compliance – with a risk-based approach, aligned to GAMP 5 (2nd edition) and FDA’s Computer Software Assurance guidance.
You get the validation package
Each AI-assisted feature operates within a validated process, with audit trails, traceability and validation documentation available for inspection – the same approach applied across the platform.
Monitored after go-live
Validation isn’t a one-time event. AI performance is monitored over time – drift detection, anomaly review and periodic re-verification – so quality doesn’t quietly degrade between inspections.
Aligned with EMA guidance on good AI practice: an AI output isn't accepted just because a machine produced it — qualified people weigh the reasoning, the source data and what could go wrong, and the final decision stays with authorized humans.
What Inspectors May Ask About AI
Common questions about AI in GxP — and the evidence a validated process should be able to provide.
| The question | What you can show |
|---|---|
| "How do you know the AI is doing what it should?" | A validation package covering the whole business process — risk assessment, requirements, test evidence and traceability, aligned to GAMP 5 and FDA CSA. |
| "Who decided to accept this AI output?" | The audit trail: every AI suggestion, the human who reviewed it, and the confirm-or-correct action they took, with timestamp and identity. |
| "Why did the AI classify this the way it did?" | The recorded rationale and supporting evidence behind the suggestion – visible for review, not a black-box verdict. |
| "What happens when the AI is wrong?" | Built-in controls, documented human oversight and a defined process for documents that do not classify – bad scans, wet-ink originals and edge cases. |
| "How do you keep it accurate over time?" | Post-go-live monitoring: AI accuracy is tracked over time, unusual results are reviewed, and performance is re-checked on a regular schedule – with people, not the AI, deciding on any changes. |
| "Where does our data go?" | Dedicated servers in your region, separated from other organizations’ data and not used to train shared models. |
The question
"How do you know the AI is doing what it should?"
What you can show
A validation package covering the whole business process — risk assessment, requirements, test evidence and traceability, aligned to GAMP 5 and FDA CSA.
The question
"Who decided to accept this AI output?"
What you can show
The audit trail: every AI suggestion, the human who reviewed it, and the confirm-or-correct action they took, with timestamp and identity.
The question
"Why did the AI classify this the way it did?"
What you can show
The recorded rationale and supporting evidence behind the suggestion – visible for review, not a black-box verdict.
The question
"What happens when the AI is wrong?"
What you can show
Built-in controls, documented human oversight and a defined process for documents that do not classify – bad scans, wet-ink originals and edge cases.
The question
"How do you keep it accurate over time?"
What you can show
Post-go-live monitoring: AI accuracy is tracked over time, unusual results are reviewed, and performance is re-checked on a regular schedule – with people, not the AI, deciding on any changes.
The question
"Where does our data go?"
What you can show
Dedicated servers in your region, separated from other organizations’ data and not used to train shared models.
AI proposes – your team decides
AI outputs can still be wrong or overconfident. The control model is designed to detect or route errors before finalization through configured checks, a complete audit trail and authorized human oversight. AI supports automation and decision-making; accountability remains with users and process owners.
Trusted With Inspection-Critical Work
The teams already running safety, quality and documentation on Flex Databases – the areas where "validated and inspection-ready" isn't optional.
"Technology plays a critical role in how we manage safety data. A validated, flexible system allows us to stay inspection-ready while supporting both small and growing pharmacovigilance portfolios."
Ajay Sadhwani
SVP Operations, Harbor Clinical
"One major advantage is the ability to create specific reports ourselves. Additionally, we value that our feature requests are taken seriously by Flex Databases and added to their development roadmap."
Ulrike Schorr-Neufing
Director Personnel & Qualification, ABX-CRO
"I have been with Agenus for over 26 years. Over those 26 years, our Flex CTMS and TMF implementation is the smoothest and fastest implementation of a validated IT system we have done."
Craig Winter
CIO, Agenus Inc.
Validation approach aligned with EMA's reflection paper on the use of AI in the medicinal product lifecycle and FDA's guidance on Computer Software Assurance — external reference points, not our own marks.
Current Capabilities and What Comes Next
AI at Flex Databases grows the same way the platform does – one genuinely useful, validated capability at a time. New AI features join the same platform and the same validation discipline as they’re released.
Live today
TMF filing and classification, AI-assisted medical coding, ML-powered signal detection, and AI quiz generation – available in production within controlled, validated workflows.
The same principles apply
New capabilities follow the same three principles: practical value, validation appropriate to GxP use and human-in-the-loop.
Shaped by your feedback
Client requests reach our roadmap directly – much of what we build next will come from the teams already using Flex every day.
AI in Clinical Trials: Frequently Asked Questions
Talk to us. See it yourself
Skip the generic demo. A specialist who works with this platform every day will walk you through it live, answer the hard questions directly, and show you exactly how it would run in your organization — not a script, not a slide deck.