mlsanityData to 7 Oct 2026

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

arXiv 2502.21236 ↗First posted 28 Feb 2025cs.AI cs.CL cs.HCcited by 0

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Abstract (arXiv)

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.

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References · 17

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  12. 2024Differentially Private Synthetic Data via Foundation Model APIs 2: Textcited by 74
5 more
  1. 2023Privacy-Preserving In-Context Learning for Large Language Modelscited by 50
  2. 2024Polaris: A Safety-focused LLM Constellation Architecture for Healthcarecited by 27
  3. 2024CIDAR: Culturally Relevant Instruction Dataset For Arabiccited by 17
  4. 2024Toward Large Language Models as a Therapeutic Tool: Comparing Prompting Techniques to Improve GPT-Delivered Problem-Solving Therapycited by 5
  5. 2024Domain-Specific Improvement on Psychotherapy Chatbot Using Assistantcited by 1

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