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

Formative Evaluation of Digital Health Interventions (3407) Artificial Intelligence (AI) in Medical Education (225) Generative Language Models Including ChatGPT (785) AI Language Models in Health Care (236) Neurology and Neurosciences (300) Patient Education in Nursing (10)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/51383, first published .
Optimizing ChatGPT’s Interpretation and Reporting of Delirium Assessment Outcomes: Exploratory Study

Optimizing ChatGPT’s Interpretation and Reporting of Delirium Assessment Outcomes: Exploratory Study

Optimizing ChatGPT’s Interpretation and Reporting of Delirium Assessment Outcomes: Exploratory Study

Authors of this article:

Yong K Choi1 Author Orcid Image ;   Shih-Yin Lin2 Author Orcid Image ;   Donna Marie Fick3 Author Orcid Image ;   Richard W Shulman4, 5 Author Orcid Image ;   Sangil Lee6 Author Orcid Image ;   Priyanka Shrestha7 Author Orcid Image ;   Kate Santoso2 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

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  3. Lokadjaja M, Kho J, Schulz P, Goh W. Large Language Models and their Applications in Mental Health: A Scoping Review (Preprint). JMIR Mental Health 2025 View
  4. Zeng Y, Xie H, Gu F, Chen J, Zhang G, Jiang X, Lau S. Artificial intelligence agent for delirium screening among patients in oncology and cardiac intensive care units: a proof-of-concept study. European Journal of Cardiovascular Nursing 2026 View