Tuberculosis (TB) is the leading cause of death from an infectious disease globally, with the highest burden in low- and middle-income countries. In these regions, limited healthcare access and high patient-to-provider ratios impede effective patient support, communication, and treatment completion. To bridge this gap, we propose integrating a specialized Large Language Model into an efficacious digital adherence technology to augment interactive communication with treatment supporters. This AI-powered approach, operating within a human-in-the-loop framework, aims to enhance patient engagement and improve TB treatment outcomes.
@article{arxiv.2502.21236,
title = {Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication},
author = {Daniil Filienko and Mahek Nizar and Javier Roberti and Denise Galdamez and Haroon Jakher and Sarah Iribarren and Weichao Yuwen and Martine De Cock},
journal= {arXiv preprint arXiv:2502.21236},
year = {2025}
}