Clinical trial recruitment has traditionally missed diverse populations, leading to incomplete treatment data for various demographics. This underrepresentation affects the ability to understand how medications work for different groups, including women, older adults, and ethnic minorities. AI could streamline participant screening while increasing diversity, ensuring broader health insights. The regulatory landscape is evolving, encouraging pharmaceutical companies to adopt diverse data approaches that enhance product efficacy and marketability. Traditional recruitment methods are insufficient, making it necessary to implement innovative solutions to capture a wider range of patient demographics.
The blunt answer is you might not always know how a new drug will work across different populations if clinical trial participants are not diverse.
Pharmaceutical companies realize that diverse data leads to better, more marketable products, benefiting both consumers and the healthcare industry.
Women have been less likely to participate in cardiovascular clinical trials despite heart disease being their leading cause of death, leading to a significant knowledge gap.
AI has the potential to change clinical trial recruitment by efficiently screening diverse populations and addressing the shortcomings of traditional methods.
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