Elizabeth Britton Chahine
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Elizabeth Britton Chahine has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Agena Davenport-Nicholson
Emory University, Atlanta, GA, United States
1 shared publication - Azra Ismail
Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, USA
1 shared publication - Jasmine Zhou
Emory University, Atlanta, GA, United States
1 shared publication - Nadi Nina Kaonga
Emory University School of Medicine
1 shared publication - Roshini Deva
Georgia Institute of Technology, Atlanta, Georgia, USA
1 shared publication - Selen Bozkurt
Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States
1 shared publication
Latest Publications
- Open Access
- 15 May 2025
- Published by IOS Press
- Vol. 327, 1175-1179
A Mixed-Methods Evaluation of LLM-Based Chatbots for Menopause
The integration of Large Language Models (LLMs) into healthcare settings has gained significant attention, particularly for question-answering tasks. Given the high-stakes nature of healthcare, it is essential to ensure that LLM-generated content is accurate and reliable to prevent adverse outcomes. However, the development of robust evaluation metrics and methodologies remains a matter of much debate. We examine the performance of publicly available LLM-based chatbots for menopause-related queries, using a mixed-methods approach to evaluate safety, consensus, objectivity, reproducibility, and explainability. Our findings highlight the promise and limitations of traditional evaluation metrics for sensitive health topics. We propose the need for customized and ethically grounded evaluation frameworks to assess LLMs to advance safe and effective use in healthcare.
- 15 May 2025
- Published by IOS Press
- Vol. 327, 1175-1179
Open AccessA Mixed-Methods Evaluation of LLM-Based Chatbots for Menopause
The integration of Large Language Models (LLMs) into healthcare settings has gained significant attention, particularly for question-answering tasks. Given the high-stakes nature of healthcare, it is essential to ensure that LLM-generated content is accurate and reliable to prevent adverse outcomes. However, the development of robust evaluation metrics and methodologies remains a matter of much debate. We examine the performance of publicly available LLM-based chatbots for menopause-related queries, using a mixed-methods approach to evaluate safety, consensus, objectivity, reproducibility, and explainability. Our findings highlight the promise and limitations of traditional evaluation metrics for sensitive health topics. We propose the need for customized and ethically grounded evaluation frameworks to assess LLMs to advance safe and effective use in healthcare.