Source-grounded drafting
Prepare selected CSR sections using the approved documents and report template.
AI-Assisted Clinical Reporting
Concept & initial development
From study sources
to a clearer report.
We are developing an AI-assisted workflow to help medical writers turn approved study documents into structured report drafts, with source checks and expert review at every important step.
Grounded in scientific practiceDesigned around expert reviewOpen to collaboration
The idea, in plain terms
A report should tell
the same story as its sources.
A Clinical Study Report, or CSR, brings together how a study was conducted, what was measured, what the results show and what conclusions can reasonably be drawn.
Writers must reconcile protocols, study records, bioanalytical reports, statistical outputs and established templates. Repeated manual assembly and cross-checking can make this work demanding.
Our initial focus is a reporting assistant for bioequivalence studies: helping writers assemble selected sections and check them against the approved sources.
From evidence to an editable draft
Less time assembling.
More attention to reviewing.
The proposed workflow combines source-grounded drafting with explicit checks and reviewer control.
- 01
Start with approved sources
Select the study documents, reporting template and applicable requirements.
- 02
Extract the relevant facts
Identify study descriptors, source tables, key values and document references.
- 03
Draft selected sections
Prepare repeatable report content from source evidence and the chosen template.
- 04
Check and flag discrepancies
Highlight missing information, inconsistent values and unsupported statements.
- 05
Review and export
Let designated experts resolve findings and export an editable Word draft with a findings log.
Planned capabilities
Clear drafting. Traceable checking.
A focused first product for a defined report workflow, with expansion guided by practical experience.
Numerical reconciliation
Compare selected narrative values with approved PK or statistical outputs, and flag mismatches for review.
Consistency checks
Compare study dates, subject counts, treatment names and other defined study facts across sources.
Completeness review
Highlight missing sections, references or information against the selected template and documented rules.
Source traceability
Link generated content and findings to relevant documents, pages or tables where feasible.
Editable outputs
Provide a Word draft, findings log and review record that fit an expert-led writing process.
Technical, without the complexity
Separate drafting
from verification.
Fluent text is only one part of a dependable reporting workflow.
The proposed approach uses source-grounded AI for suitable drafting tasks, alongside defined checks for selected facts, numbers and report structure. Findings would remain visible for a qualified reviewer to resolve.
Scientific interpretation, statistical assessment and final approval remain with the responsible experts.
Drafting layer
Use relevant source content and a controlled template to prepare selected narrative sections.
Checking layer
Compare defined values and study facts; record inconsistencies with supporting references.
Expert review
Medical writers, statisticians, clinical reviewers and QA retain their responsibilities.
A focused starting point
Begin with BE reporting.
Build from what we learn.
Bioequivalence (BE) studies generally compare how a test and reference product deliver an active ingredient into the body. Their reports draw together study conduct, pharmacokinetic results, statistical outputs and safety information.
The initial concept focuses on selected sections of a BE CSR and a small number of controlled templates. ICH E3 provides a recognised framework for CSR structure and content.
A longer-term direction is broader clinical and regulatory document consistency. The Common Technical Document (CTD) is a structured dossier format; expansion into wider dossier assurance would be a later, separately evaluated stage.
Read about ICH E3Our development collaboration
Domain knowledge.
Specialist AI engineering.
Developed in collaboration with our specialist AI technology partner, Mr Shabaz, an AI engineer from IIT Kharagpur.
Both initiatives are in concept and initial development. We are shaping their scope around practical scientific workflows and welcome conversations with interested teams.
A few useful answers
Understanding
the initiative.
Is this a finished reporting product?
The initiative is at the concept, planning and initial development stage. Request a demonstration to discuss current progress and the availability of a concept walkthrough.
Would it produce a final, approved CSR automatically?
The proposed output is a reviewable draft and a record of findings. Qualified writers and designated reviewers would resolve issues and retain responsibility for interpretation, approval and finalisation.
Would it re-run the statistical analysis?
The initial concept checks selected report values against approved statistical and pharmacokinetic outputs. Re-performing statistical analyses is outside the proposed initial scope.
Would it guarantee regulatory acceptance?
No. The development goal is clearer drafting, source traceability and useful pre-review checks within a defined workflow. Regulatory acceptance and the quality of the final report cannot be guaranteed by the tool.
What about confidential study information?
Controlled access, retention policies, source traceability and appropriate data-use restrictions are design priorities. Customer-specific security, contractual and validation requirements would need to be assessed before operational use.