Yi Ren, Yusheng Guo, Qingliu He, Zhixuan Cheng, Qiming Huang, Lian Yang
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引用次数: 0
Abstract
The generation of radiological results from image data represents a pivotal aspect of medical image analysis. The latest iteration of ChatGPT-4, a large multimodal model that integrates both text and image inputs, including dermatoscopy images, histology images, and X-ray images, has attracted considerable attention in the field of radiology. To further investigate the performance of ChatGPT-4 in medical image recognition, we examined the ability of ChatGPT-4 to recognize credible osteosarcoma X-ray images. The results demonstrated that ChatGPT-4 can more accurately diagnose bone with or without significant space-occupying lesions but has a limited ability to differentiate between malignant lesions in bone compared to adjacent normal tissue. Thus far, the current capabilities of ChatGPT-4 are insufficient to make a reliable imaging diagnosis of osteosarcoma. Therefore, users should be aware of the limitations of this technology.
期刊介绍:
Experimental Hematology & Oncology is an open access journal that encompasses all aspects of hematology and oncology with an emphasis on preclinical, basic, patient-oriented and translational research. The journal acts as an international platform for sharing laboratory findings in these areas and makes a deliberate effort to publish clinical trials with 'negative' results and basic science studies with provocative findings.
Experimental Hematology & Oncology publishes original work, hypothesis, commentaries and timely reviews. With open access and rapid turnaround time from submission to publication, the journal strives to be a hub for disseminating new knowledge and discussing controversial topics for both basic scientists and busy clinicians in the closely related fields of hematology and oncology.