Large Language Models Use in Tuberculosis (TB)

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Speaker: Daniel Brailita, M.D.

Recorded: October 2024

Length: Approximately 1 hour

Course Description

This course examines the emerging role of large language models (LLMs) in tuberculosis (TB) care and research. Participants will explore how these models can process and analyze vast amounts of textual data, offering predictive insights into TB treatment outcomes, improving information extraction through natural language processing (NLP) techniques, and transforming patient-provider communication.

Target Audience

This course is designed for allied health professionals, nurses, nurse practitioners, physicians, physician assistants, resident/fellows, and healthcare professionals working in a public healthcare setting.

Learning Objectives

  • Recognize the potential of large language models (LLMs) to analyze textual data and provide predictive insights into tuberculosis (TB) treatment outcomes. 
  • Identify how natural language processing (NLP) techniques improve information extraction from unstructured data, such as medical records, in TB care. 
  • Explain how LLMs can improve patient-provider communication. 
  • Describe ethical challenges raised by use of LLMs.