Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication

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{filienko2025_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 }
}