Is AI Making Clinical Trials More Equitable?

As the healthcare industry strives for greater diversity and inclusion in clinical research, AI presents both opportunities and challenges in addressing long-standing inequities. 

On one hand, AI-powered tools offer the potential to identify and reach underrepresented populations more effectively, analyze vast datasets to uncover hidden patterns of disparity, and optimize trial designs for inclusivity. These technologies can help overcome traditional barriers to participation, such as geographic limitations or lack of awareness, by enabling more targeted outreach and remote monitoring capabilities. 

On the other hand, AI systems, if not carefully designed and implemented, risk perpetuating or even amplifying existing biases.

Join us to discuss:

How are AI technologies currently being used to improve diversity and inclusion in clinical trial recruitment and retention?

What safeguards and ethical considerations are necessary to ensure that AI-driven clinical trial processes don't inadvertently introduce new biases or exacerbate existing one?

In what ways can AI help bridge the gap between clinical trial populations and real-world patient populations, and what challenges remain in achieving this goal?