Diagnostic accuracy is often challenged by variability in disease presentation across populations. This study examines the key factors contributing to atypical symptomatology and diagnostic uncertainty. Through a synthesis of current literature, key influences are identified, including genetics, comorbidities, age, sex, social, and environmental determinants of health. Healthcare disparities further complicate diagnosis, particularly in under-resourced settings. Emerging tools such as machine learning and biomarkers offer promise for improving precision but require an inclusive design to prevent the reinforcement of existing healthcare inequities. This paper highlights the need for flexible, patient-centered diagnostic models and policies that account for clinical diversity and promote health equity.