AI for Global Health: Leveraging Large Language Models for Tuberculosis Treatment Adherence
Year: 2025
Type: conference-paper
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-treatment supporter 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-theloop framework, aims to enhance patient engagement and improve TB treatment outcomes. Here, we present our development, evaluation considerations, and preliminary analysis of the LLM-powered conversational TB agent, developed specifically for Argentinian linguistic and cultural settings.
Author: Filienko, Daniil
Institution: University of Washington
Language: English
Subfield: General Health Professions
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6911952158
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