ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2023
Toward Improving Health Literacy in Patient Education Materials with Neural Machine Translation Models.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Bridging language gaps in healthcare: a systematic review of the practical implementation of neural machine translation technologies in clinical settings.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Exploring artificial intelligence chatbots in pediatric fluoride education: a cross-sectional study.Scientific reports · 2025Article
- Current applications and challenges in large language models for patient care: a systematic review.Communications medicine · 2025Article
- Advancing health equity: evaluating AI translations of kidney donor information for Spanish speakers.Frontiers in public health · 2025Article
- Primary Care Informatics: Vitalizing the Bedrock of Health Care.Journal of medical Internet research · 2024Article
- Large language models for biomedicine: foundations, opportunities, challenges, and best practices.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI.NPJ digital medicine · 2024Review
- Artificial intelligence and pain management: cautiously optimistic.Pain management · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Health literacy is the central focus of Healthy People 2030, the fifth iteration of the U.S. national goals and objectives. People with low health literacy usually have trouble understanding health information, following post-visit instructions, and using prescriptions, which results in worse health outcomes and serious health disparities. In this study, we propose to leverage natural language processing techniques to improve health literacy in patient education materials by automatically translating illiterate languages in a given sentence. We scraped patient education materials from four online health information websites: MedlinePlus.gov, Drugs.com, Mayoclinic.org and Reddit.com. We trained and tested the state-of-the-art neural machine translation (NMT) models on a silver standard training dataset and a gold standard testing dataset, respectively. The experimental results showed that the Bidirectional Long Short-Term Memory (BiLSTM) NMT model outperformed Bidirectional Encoder Representations from Transformers (BERT)-based NMT models. We also verified the effectiveness of NMT models in translating health illiterate languages by comparing the ratio of health illiterate language in the sentence. The proposed NMT models were able to identify the correct complicated words and simplify into layman language while at the same time, the models suffer from sentence completeness, fluency, readability, and have difficulty in translating certain medical terms.
Identifiers
37350905PMC10283125W4297834221What 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.