ArticleJCO clinical cancer informatics2026
Simulation-Based Evaluation of a Large Language Model-Enabled Clinical Decision Support Platform in Oncology.
Article in JCO clinical cancer informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Nurse-Led Large Language Model Chatbot for Predicting and Preventing Complications After Coronary Artery Bypass Grafting: Protocol for a Randomized Controlled Trial.JMIR research protocols · 2026Article
- Feasibility and Concordance of a Large Language Model (ChatGPT-5) as a Clinical Decision Support Tool in Gynecologic Oncology Tumor Boards: A Blinded, Multi-Observer Study.Journal of clinical medicine · 2026Article
- A New Era in Diagnosis: From Biomarkers to Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeA core clinical task is to synthesize fragmented patient data into a coherent summary to support decision making. However, electronic health record (EHR) inefficiencies burden clinicians and contribute to their cognitive overload and burnout. This study evaluated the impact of a large language model (LLM)-enabled clinical decision support (LLM-CDS) platform compared with a simulated EHR (SimEPR) on workflow efficiency and user experience in generating accurate clinical summaries during tumor board preparation and explored its applicability to consultation preparation, referrals, treatment planning, and patient communication.
methodsIn a remote, within-participant simulation, 26 oncologists from the United Kingdom, United States, Spain, and Singapore reviewed synthetic breast cancer cases and created comprehensive summaries for tumor board discussions using both LLM-CDS and SimEPR. LLM-CDS provided editable LLM-generated summaries; SimEPR required manual composition. Time to task completion was recorded. An independent reviewer assessed summary quality based on completeness, correctness, and conciseness. Participants also completed surveys on usability, cognitive load, and feature acceptability.
resultsLLM-CDS significantly reduced the summary completion time compared with SimEPR (6:55
conclusionThe LLM-CDS platform improved the efficiency and completeness of clinical summarization. Strong user acceptance and anticipated time savings underscore the potential for streamlining a range of oncology workflows.
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