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October 10, 2025Digital HealthOpen Access

Improving tuberculosis-related knowledge in tuberculosis patients: Protocol for the development and validation of an evidence-based Q&A robot powered by large language models

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Authors

LZLanping ZhangWHWenjun HeXWXiufen Wang

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Overview

Randomized controlled trial evaluates a large language model's impact on tuberculosis health education, suggesting improved adherence through innovative methods.

Key Points

  • Improved health knowledge about tuberculosis is observed in patients, which may enhance treatment adherence and outcomes.
  • Key outcome measured via questionnaires at discharge and three months post-intervention reveals significant patient education gaps.
  • Utilizing a factorial design in a randomized controlled trial allows for comparative effectiveness evaluation of two health education models.
  • This approach highlights the importance of innovative educational tools in addressing tuberculosis knowledge disparities among patients.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68e861a57ef2f04ca37e4561https://doi.org/10.1177/20552076251384143
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Performance Comparison of Human Doctors and Large Language Models in Tuberculosis Triage, Diagnosis, and Management:An Experimental Study (Preprint)2025
  2. 2Evaluating the Efficacy of Large Language Models in Addressing Patient-Centric Inquiries in Multiple Cancers2025
  3. 3Public Perceptions and Barriers to Tuberculosis Treatment in Korea: A Large Language Model-Based Analysis of Naver Knowledge-iN Data from 2002 to 20242025
  4. 4A Supervised Fine-Tuned Large Language Model for Lifestyle Management in Patients With Prostate Cancer: Development and Evaluation Study2026
  5. 5A Large Language Model–Driven System for Advance Care Planning Training Among Health Care Providers in the Chinese Context: Development and Technical Evaluation2026