Performance of a novel medical artificial intelligence large language model on supporting decision-making for emergency patients with suspected sepsis
MedGo 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.
Authors
- Lunxian Tang
- Chunxue Wang (ORCID: https://orcid.org/0000-0002-6828-3487)
- Sen Jiang (ORCID: https://orcid.org/0000-0003-2668-3180)
- Xiandong Liu
- Haitao Zhang (ORCID: https://orcid.org/0000-0002-1810-2385)
- Bo An (ORCID: https://orcid.org/0000-0003-0805-4268)
- Dongyang Zhao (ORCID: https://orcid.org/0000-0003-1926-2708)
- Tong Liu (ORCID: https://orcid.org/0009-0003-3091-4997)
- Yi Gu
Institutions
- Chinese Academy of Social Sciences (CN)
- Shanghai Medical College of Fudan University (CN)
- Shanghai East Hospital (CN)
- Institute of Ethnology (HU)
- Nanjing Medical University (CN)
Publication Details
- Journal
- World Journal of Emergency Medicine
- Published
- 2025-01-01
- DOI
- https://doi.org/10.5847/wjem.j.1920-8642.2025.095
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- Natural Science Foundation of Shanghai
- National Natural Science Foundation of China