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©The Author(s) 2025.
Artif Intell Med Imaging. Jun 8, 2025; 6(1): 107069
Published online Jun 8, 2025. doi: 10.35711/aimi.v6.i1.107069
Published online Jun 8, 2025. doi: 10.35711/aimi.v6.i1.107069
Table 3 Challenges and proposed solutions in visual language model -based ultrasound report generation
Challenge | Proposed solution |
Poor accuracy in text generation related to measurement results | Extract numerical values from ultrasound images using tools like TrOCR[15] and insert them into the report |
Suboptimal handling of correspondence between text and images | Annotate the correspondence between text and images and design mechanisms to learn these relationships |
Ineffective utilization of report templates | Use report templates as input, treat template prediction as an intermediate task, or have the model learn to modify templates |
Issues with training data volume | Split existing reports into text-image pairs and reassemble them to create pseudo-cases for training |
Ineffective utilization of historical reports | Use historical reports along with current ultrasound images as input |
Neglect of image selection task | Explicitly model the image selection process to choose representative images for the report |
Lack of utilization of ultrasound-related expertise | Fine-tune LLM models to learn this prior knowledge |
Lack of exploration of predictive tasks | Conduct in-depth research on ultrasound examination scenarios to define effective predictive tasks |
- Citation: Zeng JH, Zhao KK, Zhao NB. Artificial intelligence assisted ultrasound report generation. Artif Intell Med Imaging 2025; 6(1): 107069
- URL: https://www.wjgnet.com/2644-3260/full/v6/i1/107069.htm
- DOI: https://dx.doi.org/10.35711/aimi.v6.i1.107069