Detecting and Mitigating Bias for Inclusive and Trustworthy Clinical Research
Updated: May 04, 2026
- Bias undermines the validity and reliability of research findings and can worsen disparities in clinical research, patient care and outcomes. This statement provides practical strategies to detect, prevent, and correct major forms of bias in clinical research including selection, attrition, and algorithmic bias and covers the use of explainable AI techniques to promote equitable use of predictive tools.
- Bias mitigation requires a rigorous, iterative, and multidisciplinary governance framework, as no single method can eliminate bias. Sustaining equity and validity relies on coordinated design safeguards, statistical correction methods, algorithmic fairness approaches, transparent reporting, and continuous empirical evaluation as technologies and study contexts evolve.