## 4 Conclusions  
This study proposed a CT scan captioning model for ICH and introduced penalty applied loss functions \(L\) w/_p_ value 1 and \(L\) w/_p_ value 10 to control the bias during the model training process. The captioning model for ICH CT scans was structured as an encoder-decoder using a 3D-CNN model and a distilGPT2, demonstrating a certain level of usability compared to previous studies. Furthermore, the impact of \(L\) w/_p_ value 1 and \(L\) w/_p_ value 10 was examined during the model training process, and it was observed that they assisted the model in including more ICH-related information in the generated texts.  
However, this study involves a captioning task on the results of 2D images representing 3D spaces captured in a dispersed manner, making the captioning task more challenging than conventional single medical image captioning. Simultaneously, it is difficult to find publicly-available ICH CT scan captioning task data and related prior studies. As a result, this study was conducted using an in-house dataset. Therefore, there are limitations to directly comparing the results of this study to those of other studies. In the future, when related gold standard datasets become publicly available, these limitations can be addressed through additional experiments. Furthermore, to the best of our knowledge, studies on 3D medical image captioning, such as the one presented in this paper, are challenging to find. Therefore, we referenced the scores reported in the ImageCLEFmedical Caption task 2022, which focused on the 2D medical image captioning task, to determine the usability of our proposed method. Because of this, when interpreting the performance of the proposed method in this paper, it is essential to take this into account.  
Additionally, we proposed applying a penalty value to the SCCE loss function and observed its effects. However, there may be a more effective way to incorporate penalties into the loss function. Experiments with changing the loss function require the model's training and evaluation process, which is a time-consuming task. Therefore, we conducted this experiment with \(p\)-values of 1 and 10, which can be remedied through various additional experiments in the future.  
In future studies, we will conduct more and various experiments to determine an appropriate \(p\)-value to guide the model, not to generate ICH-related text for the normal CT scan with the additional experiments concerning the loss functions.  
Conceptualization, Y.-S.K. and C.K.; methodology, G.-Y.K. and B.-D.O.; formal analysis, G.-Y.K. and B.-D.O.; resources, Y.-S.K.; data curation, C.K.; writing--original draft preparation, G.-Y.K.; writing--review and editing, Y.-S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (No. 2022R1A5A8019303), Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) [NO. 2021-0-02068, Artificial Intelligence Innovation Hub (Artificial Intelligence Institute, Seoul National University)], and Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) grant funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR21C0198).  
This study was performed in accordance with the Declaration of Helsinki, and it was approved by the Institutional Review Board at Chuncheon Sacred Heart Hospital (IRB No. 2021-10-012).  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflict of interest.