ArticleNPJ digital medicine2025
Synthetic data distillation enables the extraction of clinical information at scale.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- From Invisible to Enrolled: How Artificial Intelligence Is Reshaping Clinical Trial Recruitment.Pharmaceutical medicine · 2026Article
- The Performance of Large Language Models in Extracting Intestinal Symptoms From Electronic Health Records: Retrospective Observational Study.Journal of medical Internet research · 2026Observational
- Forecasting Alzheimer's disease progression via identity-preserved denoising diffusion generative adversarial network.NPJ digital medicine · 2026Article
- Measuring the gap: correlating synthetic-to-real drift with PHI de-identification performance.Genomics & informatics · 2026Article
- An increased McKibbin Index is associated with anterosuperior labral hypertrophy in the context of non-dysplastic hips.Journal of experimental orthopaedics · 2026Article
- Predicting Postpartum Hemorrhage Using Clinical Features Extracted With Large Language Models.O&G open · 2025Article
- Enhancing Patient-Trial Matching With Large Language Models: A Scoping Review of Emerging Applications and Approaches.JCO clinical cancer informatics · 2025Article
- The effect of artificial intelligence-empowered mobile health on psychological distress in women following abortion: protocol for a mixed-methods study.Frontiers in psychiatry · 2025Article
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
Large-language models (LLMs) show promise for clinical note information extraction, but deployment challenges include high computational costs and privacy concerns. We used synthetic data distillation to fine-tune smaller, open-source LLMs to achieve performance comparable to larger models while enabling local hardware deployment or reduced cloud costs. Using Llama-3.1-70B-Instruct, we generated synthetic question-answer training pairs to fine-tune smaller Llama models. We evaluated performance across three tasks: synthetic clinical trial criteria, the i2b2 2018 Clinical Trial Eligibility Challenge, and apixaban trial criteria questions. The 8B-parameter model achieved high accuracy across all tasks and sometimes outperformed the 70B-Instruct teacher model. Fine-tuning with only the most challenging questions still improved performance, demonstrating the value of targeted training. Results from 3B- and 1B-parameter models showed a clear size-performance tradeoff. This work demonstrates synthetic data distillation's potential for enabling scalable clinical information extraction.
Identifiers
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.