International Journal of Clinical Case Reports, 2025, Vol.15, No.6, 293-302 http://medscipublisher.com/index.php/ijccr 299 Meanwhile, most of them adopted retrospective cohorts, and there were still problems such as missing values, inconsistent indicators, and limited clinical variables in the data, which further affected the reliability and universality of the model, suggesting the need to conduct larger-scale, prospective, and more rigorous methodological studies (Mohsen et al., 2023; Mittal et al., 2025; Chun and Kim, 2023; Huang et al., 2025). 6.2 Lack of external verification and cross-population promotion One of the main obstacles for artificial intelligence prediction models to enter clinical practice is insufficient external validation and limited cross-population promotion capabilities. Only a few studies have adopted independent cohorts for external validation, which is particularly worrying in the context of significant differences in diabetes risk factors among different races, regions, and healthcare systems (Wang et al., 2024; Mittal et al., 2025). In the future, multi-center and multi-population studies should be given priority, and systematic external validation should be conducted to enhance the robustness and portability of the model in different scenarios (Mohsen et al., 2023; Gong et al., 2025). The lack of standardized evaluation processes and open data sharing limits the transparency, repeatability, and independent validation and benchmarking of models in research, and further indicates the need to develop and adopt consensus methodological guidelines to establish high-quality, standardized datasets to promote more standardized development in this field (Dutta et al., 2022; Chun and Kim, 2023). 7 Concluding Remarks In the future, AI-assisted diabetes prediction research should focus on expanding the application scope of the model, integrating various types of data, and making the model easier to understand. There is an urgent need to conduct large-scale, multi-regional research covering various groups of people. By establishing unified standards for data collection, model validation and reporting, problems such as significant data discrepancies and insufficient external validation can be addressed. In addition, popularizing easily understandable AI technology and integrating it with multi-omics, genetic and real-world data can further optimize risk classification and provide a basis for personalized prevention plans. To successfully apply AI models in clinical practice, it is necessary to integrate them as naturally as possible into existing medical processes, while also taking into account transparency, doctor engagement, and patient safety. The heads of medical institutions should give priority to formulating management norms for model validation, standardized use and data protection. Through cross-disciplinary cooperation among doctors, data experts and managers, they should coordinate relevant training and develop user-friendly decision-making tools to ensure that the model can be used and used fairly in all regions, especially in areas with weak medical resources. AI prediction models have great potential in enhancing the accuracy and efficiency of early screening for diabetes, and can create conditions for early intervention and improving treatment outcomes. Although it still has problems in terms of the breadth of application, standard unification and ethical norms, with the continuous innovation and standardized application of technology, AI screening tools are sure to play a key role in reducing the global pressure of diabetes and promoting the development of clinical precision medicine. Acknowledgments I extend sincere thanks to Mrs. Ding for her feedback on the manuscript. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Abnoosian K., Farnoosh R., and Behzadi M., 2023, Prediction of diabetes disease using an ensemble of machine learning multi-classifier models, BMC Bioinformatics, 24(1): 337. https://doi.org/10.1186/s12859-023-05465-z Akune Y., Kawasaki R., Goto R., Tamura H., Hiratsuka Y., and Yamada M., 2025, Cost-effectiveness of AI-based diabetic retinopathy screening in nationwide health checkups and diabetes management in Japan: a modeling study, Diabetes Research and Clinical Practice, 221: 112015.
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