AI in Clinical Data Management

This course is designed for Clinical Data Managers, Data Standards and Data Science professionals, Statistical Programmers, Biostatisticians, and professionals in quality assurance, vendor management, or clinical operations who want a structured way to understand, assess, and implement AI in clinical data management.

It works for two audiences at once: those new to AI who want an overview that cuts through the hype, and those already working on AI initiatives who need the regulatory and governance context around the technology.

Yes, a working understanding of clinical data management is expected.

This is an intermediate module. Participants should be familiar with the clinical data journey, from study setup to database lock, data validation, medical coding, and reconciliation. Our module "The Practical Use of AI in Clinical Development" (X01) is a recommended foundation; this course builds on it rather than repeating it. No prior AI expertise is required.

Estimated learning time is approximately 6 to 9 hours, depending on your pace and background. The module contains roughly 2.5 hours of video across its four chapters, with the remaining time for the worked examples, the chapter recaps, and the certification test.

Yes! Once enrolled, you can access all course materials at any time and progress at your own pace. There are no deadlines or live sessions required.

Yes! Upon completing the course content and passing the certification test, you'll receive a certificate of completion. Note: The certificate supports professional development but is not a license or regulatory credential.

Most courses either teach you to use an AI tool, or discuss AI in the abstract.

This course does something different: it gives you two practical models, the AI Use Map and the AI Control Wrapper, for understanding and governing AI as it actually appears in clinical data management, in the platforms you buy, the features built into your systems, and services that take on whole tasks.

Rather than a tour of prompts, it gives you a structured, defensible way to place any AI use case, say what it requires, and put it to work compliantly.

This course supports professionals aiming for or currently working in roles such as Clinical Data Manager, Data Standards or Data Science specialist, Statistical Programmer, Biostatistician, Quality Assurance professional, or Vendor Manager.

It strengthens competencies in AI governance, risk classification, validation, and oversight, skills that are increasingly critical as AI moves from personal tools into the systems and services that clinical trials run on.

The structured framework provided in this module helps you move from using AI yourself to assessing, governing, and implementing it responsibly across your organisation.

This is a highly practical course. It is built around two reusable models, the AI Use Map and the AI Control Wrapper, and around real use cases from across the clinical data journey, with examples from current products. The goal is to give you structures you can apply to your own systems, vendors, and processes immediately.

No. The course is written for clinical data professionals, not engineers. You will understand what each kind of AI does, how it behaves and fails, and how to govern it, without needing to build models or write code yourself.

Yes. An entire chapter is devoted to the regulatory framework, which existing rules apply (ICH E6(R3), 21 CFR Part 11, EMA computerized systems, ALCOA+, data protection), the AI-specific guidance so far and the EU AI Act, why AI is harder to validate than traditional systems, and how to keep human oversight and accountability in the right place.

They are companions. X01 teaches you to use AI tools yourself in day-to-day work. X02 steps back to the bigger picture: how AI appears across clinical data management, what the regulations expect, and how to govern and implement it responsibly. X01 is a recommended foundation, but X02 recaps the essentials so it can stand on its own.