ArticleJournal of the American Medical Informatics Association : JAMIA2022
A survey of automated methods for biomedical text simplification.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
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Who cites it
16 citing papers in PubMed, 32 citations in OpenAlex.
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review.Journal of medical Internet research · 2026Article
- Lessons from the TREC Plain Language Adaptation of Biomedical Abstracts (PLABA) track.Journal of biomedical informatics · 2026Article
- HealthContradict: Evaluating biomedical knowledge conflicts in language models.NPJ digital medicine · 2026Article
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- Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature?Proceedings of machine learning research · 2025Article
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- Benchmarking large language models for biomedical natural language processing applications and recommendations.Nature communications · 2025Article
- APPLS: Evaluating Evaluation Metrics for Plain Language Summarization.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Sentence-Aligned Simplification of Biomedical Abstracts.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2024Article
- Text and Audio Simplification: Human vs. ChatGPT.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Crowdsourcing with Enhanced Data Quality Assurance: An Efficient Approach to Mitigate Resource Scarcity Challenges in Training Large Language Models for Healthcare.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Multiple Automated Health Literacy Assessments of Written Health Information: Development of the SHeLL (Sydney Health Literacy Lab) Health Literacy Editor v1.JMIR formative research · 2023Article
- A dataset for plain language adaptation of biomedical abstracts.Scientific data · 2023Article
- Expanding Interdisciplinarity: A Bibliometric Study of Medical Education Using the Medical Education Journal List-24 (MEJ-24).Perspectives on medical education · 2023Article
- Meeting the information and communication needs of health disparate populations.Journal of the American Medical Informatics Association : JAMIA · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
objectivePlain language in medicine has long been advocated as a way to improve patient understanding and engagement. As the field of Natural Language Processing has progressed, increasingly sophisticated methods have been explored for the automatic simplification of existing biomedical text for consumers. We survey the literature in this area with the goals of characterizing approaches and applications, summarizing existing resources, and identifying remaining challenges. MATERIALS AND
methodsWe search English language literature using lists of synonyms for both the task (eg, "text simplification") and the domain (eg, "biomedical"), and searching for all pairs of these synonyms using Google Scholar, Semantic Scholar, PubMed, ACL Anthology, and DBLP. We expand search terms based on results and further include any pertinent papers not in the search results but cited by those that are.
resultsWe find 45 papers that we deem relevant to the automatic simplification of biomedical text, with data spanning 7 natural languages. Of these (nonexclusively), 32 describe tools or methods, 13 present data sets or resources, and 9 describe impacts on human comprehension. Of the tools or methods, 22 are chiefly procedural and 10 are chiefly neural.
conclusionsThough neural methods hold promise for this task, scarcity of parallel data has led to continued development of procedural methods. Various low-resource mitigations have been proposed to advance neural methods, including paragraph-level and unsupervised models and augmentation of neural models with procedural elements drawing from knowledge bases. However, high-quality parallel data will likely be crucial for developing fully automated biomedical text simplification.
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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.