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

Keywords: LLMs, TB, Responsible AI, Conversational AI, Privacy

TL;DR: Using LLMs for TB patient management in Argentina.

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.

Submission Number: 63