Related Experiment Video
Updated: May 15, 2025
03:14
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
466
Year 2023 in Biomedical Natural Language Processing: a Tribute to Large Language Models and Generative AI
Cyril Grouin1, Natalia Grabar2
1Université Paris Saclay, CNRS, LISN, 91400 Orsay, France.
Yearbook of Medical Informatics
|April 8, 2025
Summary
The top 2023 biomedical Natural Language Processing (NLP) papers focus on language models for data augmentation and domain adaptation. These advancements are crucial for improving AI in healthcare and analyzing diverse data sources.
Area of Science:
- Biomedical Natural Language Processing (NLP)
- Artificial Intelligence in Healthcare
Background:
- The field of biomedical Natural Language Processing (NLP) is rapidly evolving.
- Identifying high-impact publications is crucial for tracking advancements.
Purpose of the Study:
- To identify and analyze the best scientific publications in biomedical NLP from 2023.
- To highlight current trends and key research areas within the field.
Main Methods:
- Systematic search of Medline and ACL anthology databases.
- Automatic scoring and manual shortlisting of candidate papers.
- Adjudication process with external reviewer assessment and editor selection.
Main Results:
- Two top papers were selected from 2,148 publications.
- Selected papers focus on language models for data augmentation, domain adaptation, and distillation.
- Applications include social media content and electronic health records using deep learning.
Conclusions:
- 2023 trends include classical NLP tasks, medical education, chatbots, and generative AI.
- Specific issues addressed are cancer, COVID-19 (including post-conditions), and mental health.
- Advancements in language models facilitate processing non-English languages and language portability.
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