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Digital Technology in Clinical Trials
Session 2 Track B: The Data Deluge: Automating the Delivery of Quality Clinical Data
Jonathan Raymond Andrus, MS
- Chief Strategy Officer
- Clinical Ink, United States
In recent years, with the advent of artificial intelligence (AI) and machine learning (ML), technology has been explored throughout clinical development - with a particular focus on algorithms for managing risk. The most commonly used and mature machine learning and AI applications are part of RBQM (risk-based quality management). The next big opportunity for AI/ML is within data review and analysis activities, with the goal to accelerate timelines while achieving high quality data deliverables and submissions. With the data proliferation of the past decade and data complexity only increasing with decentralization, finding concrete ways to incorporate AI and ML has the potential to reduce cycle times and dramatically enhance clinical development processes. This session will address the areas being explored and operationalized within AI/ML, and what prerequisites are needed to adopt these approaches within your own organization.
Learning Objective : At the conclusion of this session, participants should be able to:
- Discover potential benefits and opportunities for AI/ML within clinical development data activities
- Illustrate algorithms and models for applying AI/ML
- Outline foundational prerequisites needed on the path to automation
The Case for a Unified and Integrated eSource and EDC System
Raj Indupuri, MBA
- Chief Executive Officer
- eClinical Solutions, United States
Preconference Short Courses
Digital Technology in Clinical Trials Resource Kit