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arXiv · 2502.21236

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

Daniil Filienko·Mahek Nizar·Javier Roberti·Denise Galdamez·Haroon Jakher·Sarah Iribarren·Weichao Yuwen·Martine De Cock
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Abstract

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.

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Daniil Filienko, Mahek Nizar, Javier Roberti, Denise Galdamez, Haroon Jakher, Sarah Iribarren, Weichao Yuwen, Martine De Cock. 2025-02-28. Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication. https://arxiv.org/abs/2502.21236

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