Brazilian Journal of Anesthesiology
https://app.periodikos.com.br/journal/rba/article/doi/10.1016/j.bjane.2026.844768
Brazilian Journal of Anesthesiology
Original Investigation

Evaluation of two large language models for intensive care unit discharge decisions: a prospective observational cohort study

Avaliação de dois grandes modelos de linguagem para decisões de alta da unidade de terapia intensiva: um estudo de coorte observacional prospectivo

Engin İhsan Turan, Abdurrahman Engin Baydemir, Ebru Kaya, Zehra Polat Turan, Ayça Sultan Şahin

http://dx.doi.org/10.1016/j.bjane.2026.844768 Braz J Anesthesiol, vol.76, n4, p.0, 2026
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Abstract

Background

The aim of this study was to evaluate the effectiveness of two general-purpose Large Language Models (LLMs), ChatGPT and Gemini, in predicting Intensive Care Unit (ICU) discharge decisions (discharge vs. non-discharge). By comparing their outputs with decisions made by ICU physicians, we sought to determine the alignment of AI-generated recommendations with expert clinical judgment and assess their potential as decision-support tools in critical care.

Methods

This prospective observational cohort study was conducted in a tertiary ICU between September 2024 and May 2025. Adult patients (≥ 18 years) requiring ICU discharge decisions were included. Standardized clinical prompts were generated from electronic health records and input into ChatGPT and Gemini. The models’ binary discharge decisions were compared to those of ICU physicians. Model performance was assessed using accuracy, sensitivity, specificity, F1 score, Cohen’s kappa, and McNemar’s test. Discharge was defined as the positive class for all diagnostic performance analyses.

Results

A total of 398 patients were analyzed. ChatGPT demonstrated higher accuracy than Gemini (87.2% vs. 66.3%), with higher sensitivity (85.9% vs. 46.9%) and F1 score (0.890 vs. 0.628), whereas Gemini showed higher specificity (96.2% vs. 89.2%). Agreement with clinician decisions was substantial for ChatGPT (κ = 0.737, p = 0.024) and fair for Gemini (κ = 0.379, p < 0.001). Laboratory markers such as lactate, hemoglobin, and procalcitonin significantly differed between discharged and non-discharged patients.

Conclusion

Large language models may support ICU discharge decisions when guided by structured, guideline-informed prompting. ChatGPT achieved higher overall accuracy, sensitivity, and F1 score, whereas Gemini demonstrated higher specificity.

Trial registration

Externation (Discharge) of ICU, NCT06584890, registered 03 September 2024, prospectively registered, https://register.clinicaltrials.gov/prs/beta/studies/S000EVXZ00000029/recordSummary.

Keywords

Artificial intelligence; Decision making; Decision support systems, Clinical; Intensive care units; Natural language processing; Patient discharge 

Resumo

Introdução

O objetivo deste estudo foi avaliar a eficácia de dois grandes modelos de linguagem (LLMs) de uso geral, ChatGPT e Gemini, na predição de decisões de alta da Unidade de Terapia Intensiva (UTI) (alta vs. não alta). Ao comparar seus resultados com as decisões tomadas por médicos intensivistas, buscou-se determinar o alinhamento das recomendações geradas por IA com o julgamento clínico especializado e avaliar seu potencial como ferramentas de apoio à decisão em cuidados intensivos.

Métodos

Este estudo de coorte observacional prospectivo foi realizado em uma UTI terciária entre setembro de 2024 e maio de 2025. Foram incluídos pacientes adultos (≥ 18 anos) que necessitavam de decisões de alta da UTI. Prompts clínicos padronizados foram gerados a partir de prontuários eletrônicos de saúde e inseridos no ChatGPT e no Gemini. As decisões binárias de alta dos modelos foram comparadas com as dos médicos intensivistas. O desempenho do modelo foi avaliado por acurácia, sensibilidade, especificidade, escore F1, kappa de Cohen e teste de McNemar. A alta foi definida como a classe positiva para todas as análises de desempenho diagnóstico.

Resultados

Foram analisados 398 pacientes no total. O ChatGPT demonstrou maior acurácia que o Gemini (87,2% vs. 66,3%), com maior sensibilidade (85,9% vs. 46,9%) e escore F1 (0,890 vs. 0,628), ao passo que o Gemini apresentou maior especificidade (96,2% vs. 89,2%). A concordância com as decisões dos clínicos foi substancial para o ChatGPT (κ = 0,737, p = 0,024) e razoável para o Gemini (κ = 0,379, p < 0,001). Marcadores laboratoriais como lactato, hemoglobina e procalcitonina diferiram significativamente entre pacientes que receberam alta e os que não receberam.

Conclusão

Grandes modelos de linguagem podem apoiar as decisões de alta da UTI quando orientados por prompts estruturados e baseados em diretrizes. O ChatGPT alcançou maior acurácia geral, sensibilidade e escore F1, enquanto o Gemini demonstrou maior especificidade.

Registro de Ensaio Clínico

Externation (Discharge) of ICU, NCT06584890, registrado em 3 de setembro de 2024, registrado prospectivamente, https://register.clinicaltrials.gov/prs/beta/studies/S000EVXZ00000029/recordSummary.

Palavras-chave

Inteligência artificial; Tomada de decisão; Sistemas de apoio à decisão clínica; Unidades de terapia intensiva; Processamento de linguagem natural; Alta do paciente

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Submitted date:
08/01/2025

Accepted date:
05/16/2026

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