Evaluating the efficacy of large language models in predicting intensive care unit admission needs.
| Title: | Evaluating the efficacy of large language models in predicting intensive care unit admission needs. |
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| Authors: | Turan Eİ; Department of Anesthesiology, Istanbul Health Science University Kanuni Sultan Süleyman Education and Training Hospital, Istanbul, Turkey. Electronic address: enginihsan@hotmail.com.; Baydemir AE; Department of Anesthesiology, Basaksehir Cam ve Sakura City Hospital, Istanbul, Turkey.; Turan ZP; Department of Anesthesiology, Istanbul Health Science University Sisli Etfal Education and Training Hospital, Istanbul, Turkey.; Şahin AS; Department of Anesthesiology, Istanbul Health Science University Kanuni Sultan Süleyman Education and Training Hospital, Istanbul, Turkey. |
| Source: | Journal of critical care [J Crit Care] 2026 Jun; Vol. 93, pp. 155446. Date of Electronic Publication: 2026 Jan 23. |
| Publication Type: | Journal Article; Observational Study |
| Language: | English |
| Journal Info: | Publisher: W.B. Saunders Country of Publication: United States NLM ID: 8610642 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1557-8615 (Electronic) Linking ISSN: 08839441 NLM ISO Abbreviation: J Crit Care Subsets: MEDLINE |
| Imprint Name(s): | Publication: Philadelphia Pa : W.B. Saunders; Original Publication: Orlando, FL : Grune & Stratton, c1986- |
| MeSH Terms: | Intensive Care Units*/statistics & numerical data ; Patient Admission*/statistics & numerical data ; Artificial Intelligence* ; Critical Illness*; Clinical Decision-Making/methods ; Humans ; Retrospective Studies ; Female ; Male ; Middle Aged ; Aged ; Large Language Models |
| Abstract: | Background: Timely identification and transfer of critically ill patients to intensive care units (ICUs) are crucial to reducing morbidity and mortality. Delayed ICU admission is linked to higher mortality, emphasizing the need for efficient prediction systems. Artificial intelligence (AI) has shown promise in enhancing clinical decision-making. This study evaluates the efficacy of ChatGPT and Gemini AI models in predicting ICU admission needs.; Methods: This retrospective observational study analyzed data from 8043 ICU consultation cases in a tertiary hospital between January 2020 and June 2024. Clinical parameters included medication use, consultation details, ECG findings, imaging results, comorbidities, and laboratory values. Preprocessed and anonymized data were analyzed using ChatGPT and Gemini, with performance assessed through accuracy, Kappa statistic, Pearson Chi-square, and logistic regression.; Results: ChatGPT demonstrated strong predictive performance, achieving an accuracy of 93.8% and a Kappa statistic of 0.802, indicating substantial agreement with anesthesiologists' ICU decisions. Its predictions showed a highly significant association with actual clinical outcomes (Pearson Chi-Square = 5293.310, p |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Artificial intelligence; Clinical decision support systems; Critical illness; Intensive care units; Mortality; Patient admission; Prediction models |
| Entry Date(s): | Date Created: 20260124 Date Completed: 20260315 Latest Revision: 20260315 |
| Update Code: | 20260316 |
| DOI: | 10.1016/j.jcrc.2026.155446 |
| PMID: | 41579506 |
| Database: | MEDLINE |
Journal Article; Observational Study