ORIGINAL RESEARCH article
Front. Med.
Sec. Regulatory Science
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1567608
Using ChatGPT to Assist in Judging the Indications for Emergency Ultrasound: An Innovative Exploration of Optimizing Medical Resource Allocation
Provisionally accepted
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
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Introduction To assess the performance of the GPT-4O model in determining the indications for emergency ultrasound and to explore its potential for optimizing medical resource allocation. Methods This single-center retrospective observational study included 200 patients who underwent emergency ultrasound at the emergency department. Senior clinicians assessed the indications for ultrasound based on guidelines, which served as the gold standard. The medical records were input into the GPT-4O model, which generated binary classification results. The model's performance was analyzed using confusion matrices and ROC curves. Results The GPT-4O model achieved perfect sensitivity and NPV (1.00), with specificity and PPV of 0.86, and an AUC of 0.93. The model accurately identified 92 emergency cases and 93 non-emergency cases, with only 15 non-emergency cases misclassified as emergency cases. Conclusions The GPT-4O model showed excellent performance in determining the indications for emergency ultrasound, particularly in terms of sensitivity and negative predictive value. It has the potential to reduce unnecessary examinations and optimize the allocation of medical resources.
Keywords: artificial intelligence, Emergency ultrasound, GPT-4o, Medical resource allocation, point-of-care ultrasound, AI in healthcare, ultrasound
Received: 06 Mar 2025; Accepted: 29 May 2025.
Copyright: © 2025 Xu, Ye and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jiawei Wang, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
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