AI automations for clinical trials — hours instead of days

DATAN AI+ is aimed at teams in clinical trials and related areas — e.g. sponsors, CROs and roles such as data management, statistics/biometrics, clinical operations and medical writing. Wherever many documents, standards and recurring deliverables come together, the platform is of greatest use.

Tasks such as drafting and revising ECRF logics, DMP/SAP parts, table lists and accompanying documentation, extracting and summarizing information from long PDFs, and creating consistent text modules across multiple documents are typical. AI+ is particularly strong when it comes to repetitive, rule-based activities and when “pulling together” scattered information.

Both For new studies, it is suitable for the rapid creation and standardization of documents and structures. In ongoing studies, it helps in particular with updates, impact analyses, consistency checks and when working with change levels (e.g. new protocol versions, eCRF adjustments).

Depending on security and compliance requirements, you can choose EU-hosted models or locally operated options. For particularly sensitive data or strict requirements, a local/on-prem variant is useful. For many teams, an EU setup is a good mix of performance, control and effort — the model selection is made in the project based on your framework conditions.

Integrations via common file formats (e.g. DOCX, PDF, CSV), interfaces/APIs and clearly defined transfers to existing processes are typical. The aim is for AI+ not to run “on the side”, but to dock where teams are already working (templates, review processes, filing structures).

We provide support throughout the analysis and decision-making process: from study and analysis planning to statistics/modelling to reporting, interpretation and quality assurance. Depending on requirements, as sparring, project team extension or full service.

Yes We help you to operationalize issues cleanly, define endpoints and hypotheses, choose analysis strategies and avoid typical pitfalls early on — so that the study can be “evaluated” and reliable later on.