mindsON RBQM Special Edition | Responsible AI Outcomes: Validating Performance and Protecting Trial Data
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Moderators
MyRBQM Academy
by Cyntegrity
Targeted education helps clinical research teams embrace the implementation process and contribute to its success, ensuring your organization's RBQM strategy has the components it needs to meet regulatory expectations.
Established in 2019, Cyntegrity's MyRBQM® Academy units first-level online education, comprehensive instructor-led training, and practical case study workshops. Learn more at: www.academy.cyntegrity.com
Prof. Dr. Kai Eckert
Professor of AI
Mannheim Technical University
Kai heads the university’s Data & Knowledge Engineering group, where his research covers artificial intelligence evaluation, linked data, and knowledge organization. He advises lifescience partners on designing robust validation metrics - accuracy, sensitivity, specificity - that turn experimental models into production-grade decision engines.
Andrew Warrington
Sr Industry Advisor,
HC & LS, Microsoft
With more than 14 years in drug-development roles - from biomedical research to digital-therapeutic product teams - Andrew now guides European pharma clients on cloud, data, and AI strategy at Microsoft. He helps organizations deploy Azure-based platforms that broaden and accelerate their pipelines while meeting strict security and compliance demands.
Dr. Johann Proeve
Chief Scientific Officer
Cyntegrity
Johann has over 40 years of experience as a biopharmaceutical industry expert, mainly in the former role of VP of Global Clinical Data Management at Bayer Healthcare. Besides being a much sought-after industry speaker, he brings data-driven realism to the table, ensuring that goals are implementable in the real world.
Appointments
- June 25, 2025, 2:00 PM - 3:00 PM
Why attend?
AI models are already helping study teams forecast protocol risk, detect safety signals, and streamline data review. Yet boards and regulators still ask three business-critical questions:
1. Can we prove the model works?
2. Is our patient data protected?
3. What return will we see on day-to-day operations?
Join Microsoft, Mannheim University, and Cyntegrity for a concise, one-hour briefing that answers all three questions, combining industry, academic, and technology perspectives in a single session.
What you will learn
• Andrew Warrington opens the session by sizing up the promise and pitfalls of bringing AI into everyday trial work. Expect plain-language examples of where automation shaves weeks off decision cycles and a look at the Azure safeguards that keep auditors (and your DPO) happy.
• Prof. Dr. Kai Eckert then demystifies the scorecards behind “trustworthy AI.” He’ll walk through accuracy, precision, recall, and specificity, showing how each metric keeps models reproducible, fair, and regulator-ready.
• Dr. Johann Proeve wraps things up with real-world wins: Catching data discrepancies before they snowball, spotlighting under-performing sites early, and nudging teams back to protocol when they drift. He’ll also share how continuous oversight keeps the algorithms sharp long after they go live.
Key take-aways
>> A clear, field-tested metric framework for trustworthy AI.
>> Practical guidance on securing patient data while meeting evolving regulations.
>> Real-world evidence of cost, time, and quality gains from AI-enabled QbD.
Who should attend
Clinical operations leaders, quality executives, digital health owners, and data science managers who are planning to embed responsible AI in study oversight.
Looking ahead
This webinar is also a preview of our invite-only “Leadership Roundtable: Implementing QbD with AI,” which will be held in September 2025 at Microsoft’s Messeturm office in Frankfurt (Germany). Webinar participants will receive priority access to request an invitation.