TY - JOUR
T1 - Large language models
T2 - a primer and gastroenterology applications
AU - Shahab, Omer
AU - El Kurdi, Bara
AU - Shaukat, Aasma
AU - Nadkarni, Girish
AU - Soroush, Ali
N1 - Publisher Copyright:
© The Author(s), 2024.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Over the past year, the emergence of state-of-the-art large language models (LLMs) in tools like ChatGPT has ushered in a rapid acceleration in artificial intelligence (AI) innovation. These powerful AI models can generate tailored and high-quality text responses to instructions and questions without the need for labor-intensive task-specific training data or complex software engineering. As the technology continues to mature, LLMs hold immense potential for transforming clinical workflows, enhancing patient outcomes, improving medical education, and optimizing medical research. In this review, we provide a practical discussion of LLMs, tailored to gastroenterologists. We highlight the technical foundations of LLMs, emphasizing their key strengths and limitations as well as how to interact with them safely and effectively. We discuss some potential LLM use cases for clinical gastroenterology practice, education, and research. Finally, we review critical barriers to implementation and ongoing work to address these issues. This review aims to equip gastroenterologists with a foundational understanding of LLMs to facilitate a more active clinician role in the development and implementation of this rapidly emerging technology.
AB - Over the past year, the emergence of state-of-the-art large language models (LLMs) in tools like ChatGPT has ushered in a rapid acceleration in artificial intelligence (AI) innovation. These powerful AI models can generate tailored and high-quality text responses to instructions and questions without the need for labor-intensive task-specific training data or complex software engineering. As the technology continues to mature, LLMs hold immense potential for transforming clinical workflows, enhancing patient outcomes, improving medical education, and optimizing medical research. In this review, we provide a practical discussion of LLMs, tailored to gastroenterologists. We highlight the technical foundations of LLMs, emphasizing their key strengths and limitations as well as how to interact with them safely and effectively. We discuss some potential LLM use cases for clinical gastroenterology practice, education, and research. Finally, we review critical barriers to implementation and ongoing work to address these issues. This review aims to equip gastroenterologists with a foundational understanding of LLMs to facilitate a more active clinician role in the development and implementation of this rapidly emerging technology.
KW - ChatGPT
KW - artificial intelligence
KW - large language models
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85186205690&partnerID=8YFLogxK
U2 - 10.1177/17562848241227031
DO - 10.1177/17562848241227031
M3 - Review article
AN - SCOPUS:85186205690
SN - 1756-283X
VL - 17
JO - Therapeutic Advances in Gastroenterology
JF - Therapeutic Advances in Gastroenterology
ER -