ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2024
Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review.Frontiers in psychiatry · 2025Pooled it
- Performance of Large Language Models in Answering Healthcare Delivery Questions: A Quantitative Cross-Sectional Study.Health science reports · 2026Article
- A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives.Journal of healthcare informatics research · 2026Review
- Use of Automation Technologies and Data Mining in Speech Recognition for Autism.Brain and behavior · 2026Review
- AnFrontiers in psychiatry · 2026Article
- Enhancing Text Datasets With Scaling and Targeting Data Augmentation to Improve BERT-Based Machine Learners.Expert systems with applications · 2025Article
- Synthetic data trained open-source language models are feasible alternatives to proprietary models for radiology reporting.NPJ digital medicine · 2025Article
- LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data.Frontiers in artificial intelligence · 2025Article
- Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes.Journal of the American Medical Informatics Association : JAMIA · 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
4 authors.
Funding
Abstract
An important problem impacting healthcare is the lack of available experts. Machine learning (ML) models may help resolve this by aiding in screening and diagnosing patients. However, creating large, representative datasets to train models is expensive. We evaluated large language models (LLMs) for data creation. Using Autism Spectrum Disorders (ASD), we prompted GPT-3.5 and GPT-4 to generate 4,200 synthetic examples of behaviors to augment existing medical observations. Our goal is to label behaviors corresponding to autism criteria and improve model accuracy with synthetic training data. We used a BERT classifier pretrained on biomedical literature to assess differences in performance between models. A random sample (N=140) from the LLM-generated data was also evaluated by a clinician and found to contain 83% correct behavioral example-label pairs. Augmenting the dataset increased recall by 13% but decreased precision by 16%. Future work will investigate how different synthetic data characteristics affect ML outcomes.
Identifiers
38827067PMC11141799What 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.