ArticleRadiation oncology journal2023
Developing prompts from large language model for extracting clinical information from pathology and ultrasound reports in breast cancer.
Article in Radiation oncology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 3 of them syntheses that pooled it.
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Who cites it
82 citing papers in PubMed, 3 syntheses or guidelines pooled it, 123 citations in OpenAlex.
- Performance of Natural Language Processing for Information Extraction From Electronic Health Records Within Cancer: Systematic Review.JMIR medical informatics · 2025Pooled it
- Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review.JAMA · 2025Pooled it
- Utilizing large language models in breast cancer management: systematic review.Journal of cancer research and clinical oncology · 2024Pooled it
- Agentic AI workflows in anatomic pathology: Opportunities and challenges.Journal of pathology informatics · 2026Review
- Large language models decode narrative pathology reports to define clinically relevant subtypes in IgA nephropathy.Nature communications · 2026Article
- Accelerating real-world data collection using large language models in rare neoplasms: a bone sarcoma example.ESMO real world data and digital oncology · 2026Article
- Article
- Understanding uncertainty in large language model predictions of early death in critically ill patients: a conformal prediction approach.JAMIA open · 2026Article
- ChatGPT in Public Cardiovascular Healthcare: Accuracy, Limitations, and Implications.Journal of evaluation in clinical practice · 2026Article
- Multimodal Prediction of Renal Tumor Malignancy From Radiology Reports and Structured Electronic Health Records: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Extracting Cardiorespiratory Symptoms From Clinical Notes Using Open-Weight Large Language Models: Method Development and Validation Study.JMIR cardio · 2026Article
- Performance of large language models for extracting clinical data from breast cancer pathology reports: a systematic review.NPJ digital medicine · 2026Article
- Feasibility of Large Language Model-Based Standardized Virtual Patients to Support Clinical Decision-Making Training in Operative Dentistry: Mixed Methods Study.JMIR formative research · 2026Article
- Leveraging large language models to populate structured clinical case report forms from unstructured medical notes in radiation oncology.Clinical and translational radiation oncology · 2026Article
- Intelligent Support for Radiotherapy: A Review of Clinical Applications for Large Language Models.Journal of clinical medicine · 2026Review
- Application of a Natural Language Processing Framework for Data Extraction From Pathology Reports Across Multiple Cancer Types.Journal of Korean medical science · 2026Article
- Real-World Impact and Educational Effectiveness of an AI-Powered Medical History-Taking System: Retrospective Propensity Score-Matched Cohort Study.JMIR medical education · 2026Article
- Automated abstraction of clinical parameters of multiple myeloma from real-world clinical notes using large language models.BMC medical informatics and decision making · 2026Article
- Integrating Large Language Models with Deep Learning for Breast Cancer Treatment Decision Support.Diagnostics (Basel, Switzerland) · 2026Article
- Automated Tumor and Node Staging from Esophageal Cancer Endoscopic Ultrasound Reports: A Benchmark of Advanced Reasoning Models with Prompt Engineering and Cross-Lingual Evaluation.Diagnostics (Basel, Switzerland) · 2026Article
22 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
purposeWe aimed to evaluate the time and cost of developing prompts using large language model (LLM), tailored to extract clinical factors in breast cancer patients and their accuracy. MATERIALS AND
methodsWe collected data from reports of surgical pathology and ultrasound from breast cancer patients who underwent radiotherapy from 2020 to 2022. We extracted the information using the Generative Pre-trained Transformer (GPT) for Sheets and Docs extension plugin and termed this the "LLM" method. The time and cost of developing the prompts with LLM methods were assessed and compared with those spent on collecting information with "full manual" and "LLM-assisted manual" methods. To assess accuracy, 340 patients were randomly selected, and the extracted information by LLM method were compared with those collected by "full manual" method.
resultsData from 2,931 patients were collected. We developed 12 prompts for Extract function and 12 for Format function to extract and standardize the information. The overall accuracy was 87.7%. For lymphovascular invasion, it was 98.2%. Developing and processing the prompts took 3.5 hours and 15 minutes, respectively. Utilizing the ChatGPT application programming interface cost US $65.8 and when factoring in the estimated wage, the total cost was US $95.4. In an estimated comparison, "LLM-assisted manual" and "LLM" methods were time- and cost-efficient compared to the "full manual" method.
conclusionDeveloping and facilitating prompts for LLM to derive clinical factors was efficient to extract crucial information from huge medical records. This study demonstrated the potential of the application of natural language processing using LLM model in breast cancer patients. Prompts from the current study can be re-used for other research to collect clinical information.
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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.