Webinar Details:

Artificial Intelligence (AI) is rapidly transforming the landscape of clinical research. This webinar explores how AI and Machine Learning (ML) are revolutionizing protocol design, site selection, predictive analytics, and operational oversight. It provides practical insights into ethical considerations, compliance requirements, and implementation strategies.

By understanding the capabilities—and limitations—of AI in clinical trials, stakeholders can streamline operations, reduce costs, and improve patient outcomes without compromising data integrity or regulatory compliance.

The session begins with an introduction to AI and ML in clinical trials, where we clarify the foundational concepts of Artificial Intelligence (AI) and Machine Learning (ML), distinguishing between the two. This section also compares Narrow AI and General AI and discusses why these technologies are becoming indispensable in the evolving landscape of clinical research.

Next, we explore AI applications across the clinical trial lifecycle. Here, we highlight how AI enhances processes from drug discovery to diagnostics and trial execution. We also examine how real-time data capture and analysis powered by AI are streamlining operations and improving trial outcomes.

This is followed by a discussion on protocol design optimization, addressing common challenges such as rigid inclusion/exclusion criteria and lengthy IRB review times. We demonstrate how AI can refine criteria using real-world data (RWD), simulate scenarios to increase feasibility, and inform endpoint selection using historical trial data. A case study illustrates how this approach improves design efficiency.

We then transition to site selection and feasibility, a notoriously complex phase in trial planning. Using predictive modeling, AI helps assess site readiness, performance, and patient pool visibility.

The conversation continues with patient recruitment and retention, where AI plays a pivotal role in identifying dropout risks and optimizing engagement. We feature an IBM Watson case study demonstrating how machine learning improved oncology trial retention by 18% by analyzing patient behavior and psychosocial factors.

Next, we cover generative AI in regulatory submissions, examining how it accelerates the creation of protocols, SAPs, consent forms, and other trial documents. We also discuss how AI simulates regulatory feedback and facilitates faster cross-functional collaboration, ultimately streamlining submission readiness.

We then address AI ethics, transparency, and compliance. This segment underscores the importance of model explainability, human oversight, and validation. It also outlines compliance expectations from the FDA and EMA, including 21 CFR Part 11, and presents best practices for bias mitigation and trustworthy deployment. The webinar proceeds with an implementation roadmap, providing a strategic guide to AI adoption.

This is followed by a review of AI best practices and pitfalls to avoid and concluding with a summary of key takeaways and strategic impact. AI, when implemented responsibly, acts as a strategic amplifier that enhances team performance. It shortens trial timelines, improves patient matching, reduces operational costs, and—most importantly—supports ethical and compliant innovation in clinical research.

Areas Covered in the Webinar

• AI vs. ML: Definitions and Applications
• Protocol Design using AI
• AI-Driven Site Selection
• Predictive Analytics and Risk Mitigation
• Generative AI for Regulatory Documentation
• Real-World Case Studies
• Ethics and Regulatory Oversight (FDA, EMA)
• Implementation Framework and Best Practices
• Common Pitfalls and Compliance Traps
• Strategic Role of AI in Team Empowerment

Fee:
$385 for one person
$700 2-5 people
$999 6-10 people