ArticleNPJ digital medicine2024
A critical assessment of using ChatGPT for extracting structured data from clinical notes.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 152 papers, 3 of them syntheses that pooled it.
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Who cites it
152 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Performance and improvement strategies for adapting generative large language models for electronic health record applications: A systematic review.International journal of medical informatics · 2026Pooled it
- Large Language Models in Lung Cancer: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Harmonizing Multi-Institutional Clinical Documentation Using Natural Language Processing in Neurofibromatosis Type 1.Neurology. Clinical practice · 2026Observational
- Automatic Conversion of NICE Guidelines to an Executable Computational Model Using Large Language Models.Learning health systems · 2026Article
- Artificial Intelligence-Generated Electronic Medical Record Summarization in Breast Surgical Oncology.Annals of surgical oncology · 2026Article
- Article
- Commercial large language models for oral cavity cancer staging using descriptive pre-treatment MRI reports: ready for standalone use in clinical practice?Insights into imaging · 2026Article
- Automated Identification of Complex Percutaneous Coronary Intervention from Cardiac Catheterization Reports using Large Language Models.medRxiv : the preprint server for health sciences · 2026Article
- Fine-Tuning Large Language Models for Structured Extraction of Infectious Disease-Related Information From Clinical Notes in Japanese Primary Care: Development and Internal Validation Study.JMIR formative research · 2026Article
- Hierarchical multi-label structuring of Japanese SOAP clinical notes with large language models.Scientific reports · 2026Article
- Accuracy of ChatGPT-4o in Identifying Anatomical Structures on Cadaveric Images: A Practical Anatomy Examination Study.Clinical anatomy (New York, N.Y.) · 2026Article
- Comparative evaluation of radiological anatomy knowledge and accuracy of ChatGPT-5, Gemini 2.5, and Grok 4 across normal and thinking modes.Anatomical sciences education · 2026Article
- Agentic systems in computational pathology: architectures, evidence, and translational challenges.Journal of translational medicine · 2026Review
- Precision oncology meets Generative AI: assessing large language models in multidisciplinary GIST tumor boards.BMC cancer · 2026Article
- A multidimensional benchmarking framework for large language models in oncologic decision making.Scientific reports · 2026Article
- Analysis of the Validity of ChatGPT in the Assessment of Perceived Stress and Psychological Distress Among University Students: A Cross-Sectional Study.The Psychiatric quarterly · 2026Article
- Review
- Retrospective Cohort Study: Extracting Coexisting Background Breast-Lesion Features from Stage I-III Invasive Breast Cancer.Cancers · 2026Article
- Prognostic data extraction harnessing a privacy-preserving large language model: a clinician-AI collaborative retrospective evaluation in head and neck oncology.NPJ precision oncology · 2026Article
92 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors.
Funding
Abstract
Existing natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training. This study aims to evaluate ChatGPT's capacity to extract information from free-text medical notes efficiently and comprehensively. We developed a large language model (LLM)-based workflow, utilizing systems engineering methodology and spiral "prompt engineering" process, leveraging OpenAI's API for batch querying ChatGPT. We evaluated the effectiveness of this method using a dataset of more than 1000 lung cancer pathology reports and a dataset of 191 pediatric osteosarcoma pathology reports, comparing the ChatGPT-3.5 (gpt-3.5-turbo-16k) outputs with expert-curated structured data. ChatGPT-3.5 demonstrated the ability to extract pathological classifications with an overall accuracy of 89%, in lung cancer dataset, outperforming the performance of two traditional NLP methods. The performance is influenced by the design of the instructive prompt. Our case analysis shows that most misclassifications were due to the lack of highly specialized pathology terminology, and erroneous interpretation of TNM staging rules. Reproducibility shows the relatively stable performance of ChatGPT-3.5 over time. In pediatric osteosarcoma dataset, ChatGPT-3.5 accurately classified both grades and margin status with accuracy of 98.6% and 100% respectively. Our study shows the feasibility of using ChatGPT to process large volumes of clinical notes for structured information extraction without requiring extensive task-specific human annotation and model training. The results underscore the potential role of LLMs in transforming unstructured healthcare data into structured formats, thereby supporting research and aiding clinical decision-making.
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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.