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

Clinical Information and Decision Making (3938) New Methods (1882) mHealth in a Clinical Setting (1232) Decision Support for Health Professionals (2382) Machine Learning (3369) Artificial Intelligence (5081) Data Science (730)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29701, first published .
AI brain analyzing brain scan in futuristic hospital, showing tumor.

Prompt-Sensitive Decision Behavior of Large Language Models in Intensive Care Unit Mortality Prediction for Spontaneous Intracerebral Hemorrhage: Comparative Benchmarking Study

Prompt-Sensitive Decision Behavior of Large Language Models in Intensive Care Unit Mortality Prediction for Spontaneous Intracerebral Hemorrhage: Comparative Benchmarking Study

Authors of this article:

Jinn-Rung Kuo1, 2, 3 Author Orcid Image ;   Guan-Yu Chen3 Author Orcid Image ;   Xiao-Han Vivian Yap3 Author Orcid Image ;   Chao-Chien Li2 Author Orcid Image ;   Yung-De Kuo1 Author Orcid Image ;   Chung-Feng Liu2 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Mintz N, Erekat A, Mahta A, Kummer B, Reznik M. The authors reply to: Evaluating LLM-based prognostication in intracerebral hemorrhage: prompting and clinician benchmarking. Journal of the Neurological Sciences 2026:126229 View
  2. Wang Y. Evaluating LLM-based prognostication in intracerebral hemorrhage: Prompting and clinician benchmarking. Journal of the Neurological Sciences 2026:126230 View