Introduction
This demo aims to showcase the potential of language models fine-tuned using a meticulously curated dataset of structured MRI radiology reports for the examination of nasopharyngeal carcinoma (NPC). Our team has a proven track record in researching the role of AI for the early detection of NPC, having developed an AI system that achieves high sensitivity and specificity, both exceeding 90%. However, the explainability of the system remains a significant hurdle for clinical application. This challenge is not unique to our system but is pervasive in the development of AI for radiology. Therefore, in this pilot study, we investigate the capacity of language models to comprehend the context of the disease. Our aim is to explore the integration of language models into our existing system to enhance its explainability.
Affliations
- Dr. M.Lun Wong, Dept. Imaging and Interventional Radiology. The Chinese University of Hong Kong
Disclaimer
This software is provided as is and it is not a clinically validated software. The authors disclaim any responsibility arising as a consequence from using this demo.
Masked work prediction
Enter any sentence. Use the token [MASK] to see what the model predicts.
Our examples:
| Input masked sequence | Ground-truth masked word |
|---|---|
| A 2cm metastatic node is present in the right upper internal jugular chain [MASK] to the vein. | posterior |
| The skull base and associated foraminal and [MASK] are unremarkable. | fissures |
| The cavernous sinus and sections of the [MASK] included on the scan are unremarkable | cranium |