Aldwean, A., & Tenney, D. (2024, April), Large Language Models in Healthcare: Bridging the Gap between Performance Evaluation and Socio-Ethical Implications  Paper presented at 2024 ASEE North East Section, Fairfield, Connecticut. 10.18260/1-2--45771

\bibitem{asee_peer_45771}
  Aldwean, A., \& Tenney, D. (2024, April), \emph{Large Language Models in Healthcare: Bridging the Gap between Performance Evaluation and Socio-Ethical Implications}
  Paper presented at 2024 ASEE North East Section, Fairfield, Connecticut.
  10.18260/1-2--45771

Abdullah Aldwean and Dan Tenney.  "Large Language Models in Healthcare: Bridging the Gap between Performance Evaluation and Socio-Ethical Implications".  2024 ASEE North East Section, Fairfield, Connecticut, 2024, April.  ASEE Conferences, 2024.  https://peer.asee.org/45771 Internet.  15 Dec, 2024

\bibitem{asee_peer_45771}
  Abdullah Aldwean and Dan Tenney.
  "Large Language Models in Healthcare: Bridging the Gap between Performance Evaluation and Socio-Ethical Implications".
  \emph{2024 ASEE North East Section, Fairfield, Connecticut, 2024, April}.
  ASEE Conferences, 2024.
  https://peer.asee.org/45771 Internet.  15 Dec, 2024

@INPROCEEDINGS{asee_peer_45771
author = "Abdullah Aldwean and Dan Tenney"
title = "Large Language Models in Healthcare: Bridging the Gap between Performance Evaluation and Socio-Ethical Implications"
booktitle = "2024 ASEE North East Section"
year = "2024"
month = "April"
address = "Fairfield, Connecticut"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/45771}
number = {10.18260/1-2--45771}
}

TY  - CPAPER
AB  - Utilizing large language models (LLMs), such as the Bidirectional Encoder Representations Transformer (BERT), presents an opportunity to revolutionize the healthcare experience by enhancing patient engagement, facilitating medical education, and improving the overall healthcare service outcomes. However, integrating large language model solutions in a highly regulated industry such as healthcare poses many challenges to healthcare decision-makers due to the high level of uncertainty, the complexity, and the potential social and ethical implications. Therefore, conducting thorough evaluations of LLM-based systems to ensure their ability to achieve intended goals securely, ethically, and safely is critical for healthcare organizations. In this paper, we reviewed the recent advancements in LLM evaluation fronts, mainly focusing on the performance evaluation of medical LLMs in the healthcare domain. We highlighted the potential opportunities and limitations of utilizing these advanced technologies in the context of clinical services. Additionally, we propose a comprehensive framework that integrates various evaluation aspects to better meet the unique requirements of LLMs adoption in healthcare. This framework aims to facilitate the adoption decision-making process by ensuring the utilization of the LLM's potential while holding high standards of safety, security, and ethical practice. This paper contributes to the knowledge by providing researchers, decision-makers, and healthcare practitioners with valuable insights into important aspects that should be considered in LLMs adoption decisions in the healthcare domain.
AU  - Abdullah Aldwean
AU  - Dan Tenney
CY  - Fairfield, Connecticut
DA  - 2024/04/19
PB  - ASEE Conferences
TI  - Large Language Models in Healthcare: Bridging the Gap between Performance Evaluation and Socio-Ethical Implications
UR  - https://peer.asee.org/45771
DO  - 10.18260/1-2--45771
ER  -