Key Considerations for Selecting AI Vendors in Clinical Research
The promise of AI in clinical trials is undeniable. Faster timelines, automated processes, improved data quality, and real-time insights are transforming how biopharmaceutical and medical device companies conduct research. But with great promise comes great complexity and the critical need to choose the right technology partner.
What You’ll Find in This Guide
This resource explores practical approaches to AI vendor evaluation in clinical research settings. We’ll walk you through assessment frameworks, documentation practices, and partnership management strategies that organizations have developed as AI becomes more integral to clinical trials.
You’ll discover:
- Structured approaches to vendor evaluation and assessment
- Key areas where organizations typically gather vendor information
- Strategies for building and maintaining effective partnerships
Whether you’re exploring AI implementation in clinical research, developing vendor evaluation processes, or looking to understand how other organizations approach AI vendor assessment and partnership management, this resource has something valuable for you.
About This Guide
This guide represents an exploration of considerations commonly evaluated across the industry when selecting AI vendors for clinical research. We’ve designed it as a practical resource to help organizations think through relevant factors, though it’s not intended to provide regulatory advice or prescriptive guidance.
Contributing to Industry Learning
The clinical research industry continues to evolve in its approach to AI implementation. By sharing common considerations and frameworks, we can all contribute to collective learning while recognizing that each organization must develop its own specific requirements and evaluation processes. We hope this resource contributes to the ongoing conversation about AI vendor selection in clinical research.
