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

TL;DR

This paper proposes integrating a specialized large language model into digital adherence tools to improve clinician-patient communication and support TB treatment in low-resource settings.

Contribution

It introduces a novel AI-powered communication system using large language models within a human-in-the-loop framework for TB care.

Findings

Enhanced patient engagement through AI communication

Potential for improved treatment adherence

Framework adaptable to other infectious diseases

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.