ArticleWorld journal of emergency medicine2025
Performance of a novel medical artificial intelligence large language model on supporting decision-making for emergency patients with suspected sepsis.
Article in World journal of emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial intelligence and emergency physicians in the USA: enhancing clinical excellence while preserving physicians' judgment.World journal of emergency medicine · 2026Article
- Application, challenges and prospects of artificial intelligence in acute poisoning management.World journal of emergency medicine · 2026Article
- Mechanisms of acquired resistance to osimertinib in EGFR T790M-Positive lung adenocarcinoma.Frontiers in genetics · 2026Article
- Beyond the chain of survival: a scoping review of artificial intelligence applications in cardiac arrest.World journal of emergency medicine · 2026Review
- Comparative performance of ChatGPT and DeepSeek in interpreting the 2025 ESICM guidelines on sepsis fluid therapy.Digital healthArticle
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Authors and funding
9 authors.
Funding
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Abstract
backgroundLarge language models (LLMs) are being explored for disease prediction and diagnosis; however, their efficacy for early sepsis identification in emergency departments (EDs) remains unexplored. This study aims to evaluate MedGo, a novel medical LLM, as a decision-support tool for clinicians managing patients with suspected sepsis.
methodsThis retrospective study included anonymized medical records of 203 patients (mean age 79.9±10.2 years) with confirmed sepsis from a tertiary hospital ED between January 2023 and January 2024. MedGo performance across nine sepsis-related assessment tasks was compared with that of two junior (<3 years of experience) and two senior (>10 years of experience) ED physicians. Assessments were scored on a 5-point Likert scale for accuracy, comprehensiveness, readability, and case-analysis skills.
resultsMedGo demonstrated diagnostic performance comparable to that of senior physicians across most metrics, achieving a median Likert score of 4 in accuracy, comprehensiveness, and readability. MedGo significantly outperformed junior physicians (
conclusionMedGo shows significant diagnostic efficacy for sepsis and effectively supports clinicians in the ED, particularly enhancing junior physicians' performance. Our study highlights the potential of MedGo as a valuable decision-support tool for sepsis management, paving the way for specialized sepsis AI models.
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