ArticleYearbook of medical informatics2024
Year 2023 in Biomedical Natural Language Processing: a Tribute to Large Language Models and Generative AI.
Article in Yearbook of medical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis 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.
methodsWe 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.
resultsWe 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.
conclusionTrends 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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.