Bridging the gap: Leveraging AI to enhance access to clinical trials for underrepresented and underserved populations with cancer.

person Avital Gaziel Leal Health (formerly Trialjectory), Clifton, NJ info_outline Avital Gaziel, Yelena Lapidot, Tzvia Bader

Authors person Avital Gaziel Leal Health (formerly Trialjectory), Clifton, NJ info_outline Avital Gaziel, Yelena Lapidot, Tzvia Bader Organizations Leal Health (formerly Trialjectory), Clifton, NJ Abstract Disclosures Research Funding No funding sources reported Background: National guidelines emphasize the crucial role of clinical trials (CTs) participation for all cancer patients. Despite this, significant barriers hinder access, particularly for underrepresented populations. Patients encounter obstacles such as geographic disparities and limited knowledge of tumor genetics, which impact their access to innovative CTs in metropolitan areas and academic centers. Furthermore, the increasing requirement for specific genetic profiles poses an additional challenge. This study aims to assess the impact of digital health platforms on improving access to CTs, with a specific focus on barriers related to ethnicity, geographic location, and disease knowledge. Methods: Leal (formerly, Trialjectory) is an AI-based platform that matches cancer patients to CTs based on self-reported medical profiles. The study included a diverse cohort of 39,031 users who completed the questionnaire. The patients’ geographic distribution across the US was analyzed, considering proximity to medical/academic centers, willingness to travel, and disease knowledge. Additionally, we examined genetic testing and biomarker knowledge across various clinical and sociodemographic groups. Results: The digital platform significantly improved geographic and ethnic diversity among CT seekers. Compared to industry standards of as low as 3% CT diversity, nearly a quarter of platform users were non-whites. Users outside major metropolitan areas, where CT enrollment is traditionally lower, were also better represented. Markedly, non-whites reported a lower willingness to travel outside their residence for CT participation compared to their white counterparts (p < 0.0001). A gap in disease knowledge was more evident in minority groups (p < 0.0001), with African Americans having a lower probability of knowing their mutation status compared to other ethnic groups. Conclusions: Access to CTs is hindered by barriers such as ethnicity, location, and knowledge. AI-based platforms swiftly identify and resolve these issues, empowering healthcare and support organizations to address individual patient concerns and boost diversity in CTs. AI analysis pinpoints underrepresented populations and regions with limited trial access, paving the way for tailored education and support initiatives to encourage informed decision-making. Strategically establishing trial sites in relevant areas addresses limited travel willingness due to cultural or socioeconomic constraints. AI technologies offer a promising approach to mitigate disparities, improve disease knowledge, ensuring equitable CT access, and enhancing healthcare outcomes for underrepresented populations.