Exploring the potential of large language models in identifying metabolic dysfunction-associated steatotic liver disease: A comparative study of non-invasive tests and artificial intelligence-generated responses.

Journal: Liver international : official journal of the International Association for the Study of the Liver

PMID: 39526465

Abstract

BACKGROUND AND AIMS: This study sought to assess the capabilities of large language models (LLMs) in identifying clinically significant metabolic dysfunction-associated steatotic liver disease (MASLD).

Authors

Wanying Wu

Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Yuhu Guo

Faculty of Science and Engineering, The University of Manchester, Manchester, UK.
Qi Li

The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Congzhuo Jia

Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.

Keywords

Adult Aged Artificial Intelligence Fatty Liver Female Humans Language Large Language Models Liver Male Middle Aged Non-alcoholic Fatty Liver Disease Nutrition Surveys Reproducibility of Results ROC Curve Severity of Illness Index Ultrasonography United States

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View on PubMed Access via DOI PubMed (39526465)

Exploring the potential of large language models in identifying metabolic dysfunction-associated steatotic liver disease: A comparative study of non-invasive tests and artificial intelligence-generated responses.

Abstract

Authors

Keywords

External Resources

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