# Method for Enhancing AI Accuracy in Pressure Injury Detection Using Real and Synthetic Datasets
## 5 Conclusions  
We employed a diffusion-based generative AI model to produce synthetic data for the PI dataset, which was subsequently evaluated using the YOLOv8 model. This study aims to improve the accuracy of learning models for assessing pressure injuries. We demonstrated that the model trained on a combination of all real and synthetic datasets achieved the best performance.  
The experimental results demonstrated that combining the real dataset with the synthetic dataset significantly improved diagnostic accuracy compared to using only the real dataset. In particular, the model trained with a combination of all real and synthetic datasets achieved the best performance, with a precision of 90.56%, recall of 89.52%, F1 score of 89.87%, and a top-1 accuracy of 92.03%.  
These results suggest that increasing the dataset using synthetic data can significantly enhance model accuracy. The limitation of this study is the use of data from specific domestic hospitals, which may affect the generalizability of the results. Our findings demonstrate that models trained on both real and synthetic datasets enhance prediction accuracy and help prevent overfitting. These results have significant implications for the development of AI applications for PI assessment and management.  
In future research, it will be necessary to explore other synthetic data-generation methods and evaluate their effects on model accuracy. We plan to apply the model to additional PI datasets to assess its generalization capabilities and robustness more thoroughly. Furthermore, our implemented chatbot system could be expanded to support initial diagnosis in local healthcare facilities, where transferring patients to higher-level general hospitals is challenging. By linking the PI assessment model with the chatbot, healthcare providers in remote areas could receive AI-based diagnostic support, enabling timely and accurate evaluation. This approach would help bridge the gap between local facilities and specialized medical centers, ultimately improving patient care and accessibility. We hope that this study will enhance the performance of PI assessment models, thereby contributing to the provision of reliable AI solutions in the medical field.  
Conceptualization, J.K., M.K., H.Y., S.K. and K.H.P.; methodology, J.K., M.K. and H.Y.; software, J.K., M.K. and H.Y.; validation, J.K., M.K., H.Y. and K.H.P.; investigation, J.K., M.K. and H.Y.; resources, J.K., M.K., H.Y. and K.H.P.; data curation, J.K., M.K., H.Y. and K.H.P.;  
Figure 7: Chatbot system with enhanced artificial intelligence inference accuracy. (**a**) Assessment request. (**b**) Data upload. (**c**) Assessment result.  
writing--original draft preparation, J.K., M.K. and H.Y.; writing--review and editing, J.K., M.K., H.Y., S.K. and K.H.P.; supervision, J.K., M.K., H.Y., S.L. and S.K. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (Grant No.: 2022R1F1A1060641).  
Ethical review and approval were waived for this study by the Institutional Review Board of Suwon University (protocol number 2409-045-01, exemption confirmed on 9 September 2024) due to the use of de-identified data without personal information.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.