This paper is in the following e-collection/theme issue:

AI Language Models in Health Care (233) Public (e)Health, Digital Epidemiology and Public Health Informatics (1082) Natural Language Processing (944) Generative Language Models Including ChatGPT (781) Cancer Prognosis Models and Machine Learning (76) Lung Cancer (72) Clinical Information and Decision Making (2040) Decision Support for Health Professionals (1614)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78332, first published .
Enabling Just-in-Time Clinical Oncology Analysis With Large Language Models: Feasibility and Validation Study Using Unstructured Synthetic Data

Enabling Just-in-Time Clinical Oncology Analysis With Large Language Models: Feasibility and Validation Study Using Unstructured Synthetic Data

Enabling Just-in-Time Clinical Oncology Analysis With Large Language Models: Feasibility and Validation Study Using Unstructured Synthetic Data

Authors of this article:

Peter May1 Author Orcid Image ;   Julian Greß1, 2 Author Orcid Image ;   Christoph Seidel3 Author Orcid Image ;   Sebastian Sommer4 Author Orcid Image ;   Markus K Schuler2, 5 Author Orcid Image ;   Sina Nokodian1 Author Orcid Image ;   Florian Schröder2 Author Orcid Image ;   Johannes Jung1, 6 Author Orcid Image
Article Authors Cited by (1) Tweetations (1) Metrics

Journals

  1. Greß J, Otto G, Sommer S, Schuler M, Khan S, Schröder F, Seidel C. Artificial Intelligence Applications for Automated Data Extraction and Secondary Use of Clinical Information in Uro-oncology: A Systematic Review. European Urology Open Science 2026;86:68 View