Validating Medical Pictograms for Diagnostic Communication in Low-Literacy Regions

This study aims to validate the effectiveness of medical pictograms as an alternative communication tool for diagnostics and patient interaction in low-literacy settings in the United States and Ghana. The research compares pictogram-based within healthcare applications to determine which approach most accurately supports patient comprehension, diagnostic accuracy, and task efficiency. Conductbed within the framework of Mary Global Health’s AI-powered Patient Access Terminal (PAT) initiative, an ongoing program designed to reach and serve over 35,000 patients across multiple underserved communities in the United States, this study focuses on improving the quality and accessibility of diagnostic communication for populations with limited health literacy. By analyzing real user interactions, the study will assess whether pictogram interfaces can improve diagnosis accuracy, reduce time to understanding, and enhance patient satisfaction compared to conventional methods. Upon completion of the project, we are expected to have an evidence-based recommendations for implementing inclusive, low-literacy-friendly AI healthcare interfaces at scale, supported by statistical validation of accuracy, usability, and patient outcomes. Findings will contribute to the scientific foundation for equitable, AI-driven healthcare systems and form part of a forthcoming Registered Report submission to a peer-reviewed journal.