Year 2023 in Biomedical Natural Language Processing: a Tribute to Large Language Models and Generative AI

Abstract

OBJECTIVES: This synopsis gives insights into scientific publications from 2023 in Natural Language Processing for the biomedical domain. We present the process we followed to identify candidates for NLP's best papers and the two best papers of this year. We also analyze the current trends in the 2023 publications. METHODS: We queried two bibliographic databases (Medline and the ACL anthology) and refined the outputs through automatic scoring. We then manually shortlisted publications to review and selected candidate papers through an adjudication process. External reviewers assessed the interest of the 13 selected candidates. At last, the section editors chose the best NLP papers. RESULTS: We collected 2,148 papers published in 2023, of which two were the best and selected as part of this NLP synopsis. Both address language models and propose solutions for data augmenta-tion, domain-specific model adaptation, and model distillation. Work is done on social media con-tent and electronic health records, using deep learning approaches such as ChatGPT and large lan-guage models. CONCLUSION: Trends from 2023 cover classical NLP tasks (information extraction, text categoriza-tion, sentiment analysis), existing topics from several years (medical education), mainstream applications (Chatbots, generative approaches), and specific issues (cancer, COVID-19, mental health). Specifically for COVID-19, current researches deal with post-COVID-19 conditions, and they explore the understanding of how this pandemic has been managed and welcomed by populations. In addition, due to language models, a few works have been done to process languages other than English, especially using language portability approaches.

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