Biopharma organizations are exploring artificial intelligence across multiple use cases: patient identification, data analysis, regulatory submissions, and beyond. Along the way, they’re seeing both short- and long-term success; some changes are fast, and others are slow.

But as clinical trial leaders embrace the opportunities, they’re also contending with challenges—such as a criticism of algorithm bias and concerns around data security. With these problems jeopardizing new possibilities, experts recommend a strategic approach to AI and ML adoption that accounts for the implications of this tech now and in the future.

The following report unpacks what’s new, exploring perspectives surrounding AI and ML in clinical trials and experts’ predictions for the future. You’ll learn:

– Disruptive and incremental use cases for AI in clinical trials
– Outstanding challenges, such as algorithm bias and data security
– How sponsors are balancing the knowns and unknowns of a still-maturing technology