Bridging the Generalizability Gap: A Methodologic Framework for Enhancing Generalizability in Clinical Trials Across Underrepresented Populations

Clinical trials constitute a cornerstone of contemporary medicine and healthcare, as they generate the highest level of empirical evidence regarding the safety and efficacy of pharmacological agents and other therapeutic or preventive interventions. Although strategies to address underrepresentation exist, a critical gap in the literature is the lack of comprehensive, operationalized approaches that integrate study design, social determinants of health (SDOH) data, statistical methodologies, reporting practices, and applications of artificial intelligence and machine learning (AI/ML). To address this gap, the Perspective outlines populations that are typically underrepresented, discusses the ramifications of this underrepresentation, and presents a comprehensive set of strategies to enhance representation and promote generalizability in clinical trials. The five components detailed in the following sections are: (1) defining inclusion and exclusion criteria that balance internal and external validity and reflect the broader affected community; (2) collecting and operationalizing SDOH and demographic data to understand factors associated with treatment outcomes; (3) using statistical adjustment techniques to account for diverse representation in results; (4) ensuring comprehensive clinical trial reporting to support transparency and reproducibility; and (5) leveraging AI/ML to optimize study planning and data analysis. Plain Language Summary: Making Medical Research Work For Everyone: Clinical trials test whether new medicines and treatments are safe and effective. However, many trials do not include enough participants from key groups, such as older adults, racial and ethnic minorities, people with lower incomes, rural residents, and women. When these groups are underrepresented, we cannot be sure if the treatment will work the same way for everyone in real-world settings. This Perspective explains why this underrepresentation happens and why it matters. More importantly, it provides a practical guide to fix the problem. The authors propose five strategies: (1) using real-world health data to design fairer eligibility rules; (2) collecting and analyzing information about social and demographic factors that affect health; (3) using statistical methods to adjust for trial population imbalances; (4) following updated guidelines for transparent reporting of the clinical trial plan and results; and (5) using artificial intelligence carefully to reduce bias, not amplify it. These strategies aim to ensure that future clinical trials include the people who need the treatments most. The goal is better, fairer healthcare for all.