Harnessing the power of large language models for clinical tasks and synthesis of scientific literature.

作者信息

Bakken Suzanne

机构信息

School of Nursing, Department of Biomedical Informatics, Data Science Institute, Columbia University, New York, NY 10032, United States.

出版信息

J Am Med Inform Assoc. 2025 Jun 1;32(6):983-984. doi: 10.1093/jamia/ocaf071.

DOI:10.1093/jamia/ocaf071

PMID:40390622

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12089756/

Abstract

摘要

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本文引用的文献

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The emergence of large language models as tools in literature reviews: a large language model-assisted systematic review.

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[2]

Development and validation of the provider documentation summarization quality instrument for large language models.

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[3]

Detecting emergencies in patient portal messages using large language models and knowledge graph-based retrieval-augmented generation.

J Am Med Inform Assoc. 2025-6-1

[4]

Evaluating the effectiveness of biomedical fine-tuning for large language models on clinical tasks.

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[5]

DiMB-RE: mining the scientific literature for diet-microbiome associations.

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[6]

Enhancing systematic literature reviews with generative artificial intelligence: development, applications, and performance evaluation.

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[7]

Collaborative large language models for automated data extraction in living systematic reviews.

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Harnessing the power of large language models for clinical tasks and synthesis of scientific literature.

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