Feb. 6, 2024, 5:55 a.m. | Ziqi Yang Xuhai Xu Bingsheng Yao Shao Zhang Ethan Rogers Stephen Intille Nawar Shara Guodong G

cs.CL updates on arXiv.org arxiv.org

Despite the plethora of telehealth applications to assist home-based older adults and healthcare providers, basic messaging and phone calls are still the most common communication methods, which suffer from limited availability, information loss, and process inefficiencies. One promising solution to facilitate patient-provider communication is to leverage large language models (LLMs) with their powerful natural conversation and summarization capability. However, there is a limited understanding of LLMs' role during the communication. We first conducted two interview studies with both older adults …

applications asynchronous availability basic communication cs.ai cs.cl cs.hc healthcare healthcare providers home information language language models large language large language models llms loss messaging patient phone phone calls process provider solution telehealth

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Senior Software Engineer, Generative AI (C++)

@ SoundHound Inc. | Toronto, Canada