Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929735630585856 |
|---|---|
| author | Filienko, Daniil Nizar, Mahek Roberti, Javier Galdamez, Denise Jakher, Haroon Iribarren, Sarah Yuwen, Weichao De Cock, Martine |
| author_facet | Filienko, Daniil Nizar, Mahek Roberti, Javier Galdamez, Denise Jakher, Haroon Iribarren, Sarah Yuwen, Weichao De Cock, Martine |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_21236 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication Filienko, Daniil Nizar, Mahek Roberti, Javier Galdamez, Denise Jakher, Haroon Iribarren, Sarah Yuwen, Weichao De Cock, Martine Artificial Intelligence Computation and Language Human-Computer Interaction 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. |
| title | Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication |
| topic | Artificial Intelligence Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2502.21236 |