## 6 Conclusions  
In conclusion, this work has shown that generative AI, text-to-image synthesis, and intelligent data augmentation can be applied to overcome limitations arising from a limited number of colonoscopy images and data bias. This study demonstrated a novel way to generate a diverse and realistic number of synthetic colonoscopy images that could reflect a rather wide range of patients by improving state-of-the-art models such as CLIP, DB LoRa and SD. The comparative analysis of these synthetic images computed via FID and IS metrics, as well as the outcome of classification and segmentation models trained to utilize augmented datasets, prove the critical enhancements in AI model effectiveness and non-bias. The proposed augmentation method using original and synthetic images showed better performance than original and purely synthetic datasets for several models and evaluation metrics. This study helps address the problem of minority representation in both the identification and treatment of CRC by improving the training datasets of AI-supported colonoscopy technologies. It also pushes the point of generative AI in analyzing colonoscopy and medical imaging at large, moving with more than colonoscopy applications. However, some limitations must be investigated in subsequent studies, for example, the requirement for clinical evaluation of synthesized images and some ethical concerns about incorporating AI-generated medical data. However, this work sets a robust framework upon which future studies can build to enhance the efficiency of applying artificial intelligence to the analysis of medical images and to overcome disparities affecting health in different countries. This study recommends the use of augmented datasets in AI model training for colonoscopy assistance tools, as synthetic image generation has produced promising results. Augmented data sets improve developer resources, model accuracy, and generalization while also reducing discrimination caused by the underrepresentation of racial minorities in medical imaging data. A notable limitation of this study is the absence of human evaluation through blinded evaluation. Future work would benefit from having experienced medical professionals perform blinded comparisons between real and synthetic colonoscopy images. This expert evaluation would provide valuable information on the clinical viability of the generated images and help validate whether the synthetic data could effectively supplement medical training datasets. Clinical validation studies in real world settings are critical to determining the effectiveness of these methods and addressing potential risks before deployment. Future research should focus on large-scale clinical validation of AI models trained on augmented datasets, the development of ethical guidelines for the use of synthetic medical images in clinical and training settings, and the application of synthetic data augmentation techniques to other medical imaging modalities besides colonoscopy. General implementation requires careful consideration of practical challenges, approval processes, and integration into existing ones. Conceptualization, O.O.E.P., O.T.A. and M.M.R. ; methodology, M.M.R., F.K., O.O.E.P. and A.M.J.-O. ; software, O.O.E.P. ; validation, O.O.E.P., F.K. and M.M.R. ; formal analysis, O.O.E.P. ; investigation, O.O.E.P. ; resources, F.K. and M.M.R. ; data curation, O.O.E.P. and M.M.R. ; writing--original draft preparation, F.K., M.M.R. and O.O.E.P. ; writing--review and editing, O.T.A., A.M.J.-O., F.K. and M.M.R. ; visualization, O.O.E.P. and O.T.A. ; supervision, F.K. and M.M.R. ; project administration, F.K. and M.M.R. ; funding acquisition, F.K. and M.M.R. All authors have read and agreed to the published version of the manuscript. This work is supported in part by the National Science Foundation (NSF) under Grant No.