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

AI Language Models in Health Care (707) Clinical Information and Decision Making (3582) Public (e)Health, Digital Epidemiology and Public Health Informatics (2246) Natural Language Processing (1242) Decision Support for Health Professionals (2150) Lung Cancer (112) Cancer Prognosis Models and Machine Learning (136) Generative Language Models Including ChatGPT (1442)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78332, first published .
Doctor in blue scrubs using a tablet in a hospital hallway

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 (3) 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
  2. May P, Brookman-May S, Garrahy E, von Büren J. Sequencing AI Automation and Data Interoperability in Oncology Using a Scenario-Planning Framework Coupled With Discrete-Event Simulation: Proof-of-Concept Study. Journal of Medical Internet Research 2026;28:e92642 View
  3. Knauer M, Greß J, Kather J, May P. Augmenting Oncology Guideline Maintenance with Large Language Models: A Prospective Case Study (Preprint). JMIR AI 2026 View