ReviewNPJ digital medicine2025
Evaluation and practical application of prompt-driven ChatGPTs for EMR generation.
Review 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 14 papers, 1 of them a synthesis that pooled it.
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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large Language Models in Lung Cancer: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Review
- Automatic Speech Recognition in Healthcare in the Post-LLM Era: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Evaluating open-source LLMs for dental EMR generation.BMC oral health · 2026Article
- Comparing physician and artificial intelligence chatbot responses to preterm infant care questions posted to a public medical consultation forum: evaluation study.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- Retrieval‑augmented large language models for depression screening and suicide risk stratification.BMC psychiatry · 2026Article
- Interpretable Multimodal Molecular Language Model for Drug-Target Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Pancreatic cancer burden in China from 1990 to 2023: an analysis based on the global burden of disease study 2023.Frontiers in endocrinology · 2026Article
- Brief report: Artificial intelligence meets small cell lung cancer-integrating clinicopathological and wholeslide image data for prognostic prediction in SCLC.Frontiers in artificial intelligence · 2026Article
- Non-small cell lung cancer subtype classification based on cross-scale multi-instance learning.Scientific reports · 2025Article
- Evaluation of performance of generative large language models for stroke care.NPJ digital medicine · 2025Article
- The potential of large language models to advance precision oncology.EBioMedicine · 2025Review
- AI-based prediction of pathological risk factors in lung adenocarcinoma from CT imaging: bridging innovation and clinical practice.Frontiers in oncology · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
This study investigates the application of prompt engineering to optimize prompt-driven ChatGPT for generating electronic medical records (EMRs) during lung nodule screening. We assessed the performance of ChatGPT in generating EMRs from patient-provider verbal consultations and integrated this approach into practical tools, such as WeChat mini-programs, accessible to patients before hospital visits. The findings highlight ChatGPT's potential to enhance workflow efficiency and improve diagnostic processes in clinical settings.
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.