The practical use of AI in clinical development (TTC KW X01)
The practical use of AI in clinical development (TTC KW X01)
- Summaries, examples, and practical checklists
- Access on mobile & desktop
- Certificate of completion
- 6 months access
- Duration time: 10 hours
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Practical Use of AI in Clinical Data Management - TTC KW X01
The Practical Use of AI in Clinical Data Management course is designed for professionals who want to understand how generative Artificial Intelligence can be used responsibly and effectively in day-to-day clinical data management work. It is suitable for beginners to intermediate learners in clinical research, including Clinical Data Managers, Clinical Trial Assistants, Clinical Research Associates (CRAs), and professionals working in clinical operations, biometrics, or related functions.
This course focuses on the practical application of generative AI tools such as ChatGPT, Copilot, and similar systems. Rather than treating AI as a theoretical concept, the module shows how it can support real tasks such as searching and analysing regulatory information, improving and drafting documents, reviewing protocols, suggesting CRF fields, and reconciling data.
In a regulated clinical research environment, AI must be used with care. This module helps you understand both what AI can do and where its limitations are, so you can apply it as a reliable assistant while maintaining quality, compliance, and professional judgment.
Why Learn with TriTiCon
TriTiCon delivers clinical data management training based on extensive hands-on experience from real clinical trials and operational roles across sponsors, CROs, and life sciences organizations. The content is developed by professionals who understand both the opportunities and the constraints of working in a regulated environment.
In the context of AI, this practical perspective is essential. While many platforms discuss AI in abstract or promotional terms, TriTiCon focuses on how AI fits into real clinical development workflows, including quality management systems, data protection requirements, and professional accountability.
The X01 module is built around concrete examples from clinical data management, showing how AI can support work without replacing expertise. You will see how to combine AI efficiency with human oversight, validation, and responsibility, which is critical in clinical research.
Compared with generic AI or clinical research courses, TriTiCon’s training emphasizes job-relevant use cases, realistic expectations, and clear boundaries for responsible AI use in clinical development.
What You’ll Learn
This module provides a structured, end-to-end view of how generative AI can support clinical data management. The content is organized into five chapters that build from foundational understanding to hands-on application and responsible use.
1
Introduction to AI and Core Concepts
- What generative AI is and how it differs from predictive AI and machine learning
- Common misconceptions about AI and data usage
- Understanding AI as an information-processing machine, not a decision-maker
- Building trust through proper use, review, and validation
2
AI as a Smart Search and Analysis Assistant
- Using AI to search, analyse, and compare complex information
- Summarising regulatory guidance and identifying key changes
- Explaining new or unfamiliar concepts in a structured way
- Organising and mapping information across standards (CDASH, SDTM, ADaM, E2B)
3
AI as a Smart Writing Assistant
- Improving text for clarity, consistency, and professional tone
- Summarising long content into concise, focused messages
- Expanding bullet points or draft notes into fluent, professional text
- Supporting documentation such as Data Management Plans, specifications, and summaries
4
AI as a Personal Assistant for CDM Tasks
- Reviewing protocols for CDISC SDTM compliance
- Suggesting CRF fields with standard mappings and basic edit checks
- Supporting data merging and reconciliation across sources
- Generating structured outputs and example code for revie
5
When to Use AI – and When Not To
- Identifying good AI use cases in clinical data management
- Understanding AI output quality and limitations
- Applying ALCOA+ and data integrity principles when using AI
- Integrating AI use into the Quality Management System
Anders Mortin
Clinical Data Management Expert
TriTiCon delivers clinical data management training based on extensive hands-on experience from real clinical trials across sponsors, CROs, and life sciences organizations. The training is developed by industry professionals who work directly with clinical data, systems, documentation, and cross-functional trial teams.
Who is this course for?
This course is designed for Clinical Data Managers, Clinical Trial Assistants, CRAs, and professionals in clinical operations, biometrics, or related functions who want to understand how to use generative AI responsibly and effectively in day-to-day CDM work.
Do I need prior clinical trial experience?
No prior AI experience is required. The course starts with foundational concepts and builds to practical applications. Basic understanding of clinical development and clinical data management is recommended.
How long does the course take?
Estimated learning time is approximately 8–12 hours, depending on your pace and background.
Is it self-paced?
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.
Do I receive a certificate?
Yes! Upon completing all chapters 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 qualification.
How is this different from other CDM courses?
TriTiCon focuses on how AI fits into real clinical development workflows, including quality management systems, data protection requirements, and professional accountability. The module is built around concrete CDM examples, showing how AI can support work without replacing expertise.
What career outcomes does this support?
Yes! This module helps you understand both what AI can do and where its limitations are, so you can apply it as a reliable assistant while maintaining quality, compliance, and professional judgment. You'll learn to combine AI efficiency with human oversight and validation.
Description
Practical Use of AI in Clinical Data Management - TTC KW X01
The Practical Use of AI in Clinical Data Management course is designed for professionals who want to understand how generative Artificial Intelligence can be used responsibly and effectively in day-to-day clinical data management work. It is suitable for beginners to intermediate learners in clinical research, including Clinical Data Managers, Clinical Trial Assistants, Clinical Research Associates (CRAs), and professionals working in clinical operations, biometrics, or related functions.
This course focuses on the practical application of generative AI tools such as ChatGPT, Copilot, and similar systems. Rather than treating AI as a theoretical concept, the module shows how it can support real tasks such as searching and analysing regulatory information, improving and drafting documents, reviewing protocols, suggesting CRF fields, and reconciling data.
In a regulated clinical research environment, AI must be used with care. This module helps you understand both what AI can do and where its limitations are, so you can apply it as a reliable assistant while maintaining quality, compliance, and professional judgment.
Why Learn with TriTiCon
TriTiCon delivers clinical data management training based on extensive hands-on experience from real clinical trials and operational roles across sponsors, CROs, and life sciences organizations. The content is developed by professionals who understand both the opportunities and the constraints of working in a regulated environment.
In the context of AI, this practical perspective is essential. While many platforms discuss AI in abstract or promotional terms, TriTiCon focuses on how AI fits into real clinical development workflows, including quality management systems, data protection requirements, and professional accountability.
The X01 module is built around concrete examples from clinical data management, showing how AI can support work without replacing expertise. You will see how to combine AI efficiency with human oversight, validation, and responsibility, which is critical in clinical research.
Compared with generic AI or clinical research courses, TriTiCon’s training emphasizes job-relevant use cases, realistic expectations, and clear boundaries for responsible AI use in clinical development.
What You’ll Learn
This module provides a structured, end-to-end view of how generative AI can support clinical data management. The content is organized into five chapters that build from foundational understanding to hands-on application and responsible use.
What You'll Learn
Curriculum
1
Introduction to AI and Core Concepts
- What generative AI is and how it differs from predictive AI and machine learning
- Common misconceptions about AI and data usage
- Understanding AI as an information-processing machine, not a decision-maker
- Building trust through proper use, review, and validation
2
AI as a Smart Search and Analysis Assistant
- Using AI to search, analyse, and compare complex information
- Summarising regulatory guidance and identifying key changes
- Explaining new or unfamiliar concepts in a structured way
- Organising and mapping information across standards (CDASH, SDTM, ADaM, E2B)
3
AI as a Smart Writing Assistant
- Improving text for clarity, consistency, and professional tone
- Summarising long content into concise, focused messages
- Expanding bullet points or draft notes into fluent, professional text
- Supporting documentation such as Data Management Plans, specifications, and summaries
4
AI as a Personal Assistant for CDM Tasks
- Reviewing protocols for CDISC SDTM compliance
- Suggesting CRF fields with standard mappings and basic edit checks
- Supporting data merging and reconciliation across sources
- Generating structured outputs and example code for revie
5
When to Use AI – and When Not To
- Identifying good AI use cases in clinical data management
- Understanding AI output quality and limitations
- Applying ALCOA+ and data integrity principles when using AI
- Integrating AI use into the Quality Management System
Instructor
Anders Mortin
Clinical Data Management Expert
TriTiCon delivers clinical data management training based on extensive hands-on experience from real clinical trials across sponsors, CROs, and life sciences organizations. The training is developed by industry professionals who work directly with clinical data, systems, documentation, and cross-functional trial teams.
FAQ
Who is this course for?
This course is designed for Clinical Data Managers, Clinical Trial Assistants, CRAs, and professionals in clinical operations, biometrics, or related functions who want to understand how to use generative AI responsibly and effectively in day-to-day CDM work.
Do I need prior clinical trial experience?
No prior AI experience is required. The course starts with foundational concepts and builds to practical applications. Basic understanding of clinical development and clinical data management is recommended.
How long does the course take?
Estimated learning time is approximately 8–12 hours, depending on your pace and background.
Is it self-paced?
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.
Do I receive a certificate?
Yes! Upon completing all chapters 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 qualification.
How is this different from other CDM courses?
TriTiCon focuses on how AI fits into real clinical development workflows, including quality management systems, data protection requirements, and professional accountability. The module is built around concrete CDM examples, showing how AI can support work without replacing expertise.
What career outcomes does this support?
Yes! This module helps you understand both what AI can do and where its limitations are, so you can apply it as a reliable assistant while maintaining quality, compliance, and professional judgment. You'll learn to combine AI efficiency with human oversight and validation.