AI is now appearing across the clinical trial lifecycle, from protocol planning and feasibility to site selection, data review, reconciliation, patient-facing technologies, oversight, and reporting.
But for many clinical trial teams, the real challenge is no longer whether AI is relevant. It is knowing where AI can create meaningful value, where it may introduce unnecessary operational or compliance risk, and how to choose the right place to start.
This webinar will explore how to evaluate AI use cases in clinical trial operations based on business need, workflow fit, data readiness, explainability, human oversight, governance expectations, and implementation effort.
The session is designed to help teams move beyond AI excitement and toward smarter, more defensible decisions about where AI can support clinical trial workflows.
But for many clinical trial teams, the real challenge is no longer whether AI is relevant. It is knowing where AI can create meaningful value, where it may introduce unnecessary operational or compliance risk, and how to choose the right place to start.
This webinar will explore how to evaluate AI use cases in clinical trial operations based on business need, workflow fit, data readiness, explainability, human oversight, governance expectations, and implementation effort.
The session is designed to help teams move beyond AI excitement and toward smarter, more defensible decisions about where AI can support clinical trial workflows.
Register here
Agenda
- Why AI use-case selection matters now: The shift from AI experimentation to practical implementation in regulated clinical trial workflows.
- What makes an AI use case worth pursuing: How to assess business need, workflow impact, data readiness, explainability, operational burden, and return on effort.
- Where AI can support clinical trial workflows today : Practical examples across data review, reconciliation, patient-facing technologies, oversight, reporting, and study operations.
- Where AI can create risk or unnecessary complexity: Validation burden, hallucinations, documentation gaps, unclear ownership, weak data inputs, and over-automation.
- Human-in-the-loop vs. higher-risk automation: How to distinguish AI that supports decision-making from AI that may require stronger governance and control.
- A practical selection framework for clinical trial teams: Questions to ask before investing time, budget, and validation effort.
Featured Presenters
Tanya Du Plessis
Chief Data Strategist & Solutions Officer, Bioforum The Data Masters
Tanya has more than 18 years of experience managing various data management operational teams. She is a certified Clinical Data Manager and Project Management Professional, and she holds an M.Med.Sc in Hematology and Cell Biology from the University of the Free State. In addition, Tanya serves as a board member and plays an active role in the Society of Clinical Data Management (SCDM) EMEA Leadership Committee.
Willie Muehlhausen
Partner & Co-Founder, SAFIRA Clinical Research
Willie Muehlhausen is Partner and Co-Founder of SAFIRA Clinical Research, bringing more than 25 years of experience in clinical trial technology, with a focus on patient-facing technologies, eCOA/ePRO, decentralized trial readiness, and digital implementation in clinical research. Before co-founding SAFIRA, Willie held senior innovation and eCOA leadership roles at ICON and worked across clinical technology, product development, and implementation strategy. He is also a published author and industry contributor in electronic patient-reported outcomes and clinical trial technology.
Key Takeaways
Attendees will leave with:
- A practical lens for identifying high-value AI use cases in clinical trial workflows
- Criteria for assessing workflow fit, data readiness, explainability, and governance needs
- A clearer understanding of where AI can support human decision-making versus where it may create risk
- Practical questions to use when prioritizing, challenging, or approving AI initiatives
- A more grounded approach to balancing innovation, operational value, and inspection-readiness
Who Should Attend
- Clinical Operations leader
- Data Management leader
- Biometrics leader
- Biostatistics and Statistical Programming leaders
- Digital strategy and innovation teams in clinical development
- Quality and compliance stakeholders involved in AI evaluation
- Pharma, biotech, and medical device sponsor teams exploring AI in clinical trial workflows
- CRO, consulting, and technology teams supporting AI implementation
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