Keywords
health
Abstract
This review examines curriculum learning for governed oral-health risk communication through an evidence-centered design lens. The analysis asks how developmental risk evidence should be communicated and moderated. It treats the relevant unit as a complete pathway from data or physical observations to representation, model output, human interpretation, and accountable action. The cited literature is synthesized without inventing experiments or unreported performance values. Particular attention is given to associations being presented as individual clinical predictions. The review argues that credible translation requires explicit evidence boundaries, uncertainty-aware evaluation, author-visible traceability, and a documented route for intervention. The resulting framework supports oral-health decision support while distinguishing component promise from system readiness.
References
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. In Proceedings of the 26th International Conference on Machine Learning (pp. 41-48).
Deng, H., Zou, D., Ma, R., Luo, H., Cao, Y., & Kang, Y. (2025). Boosting the generalization and reasoning of vision language models with curriculum reinforcement learning. arXiv preprint arXiv:2503.07065.
Han, Z., Chen, W., Han, Y., Mao, R., & Qin, J. (2026). Fast diversified top-k rule discovery via user-guided embeddings. IEEE Transactions on Knowledge and Data Engineering, 38, 1739–1753.
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., & Aerts, H. J. W. L. (2012). Radiomics: Extracting more information from medical images using advanced feature analysis. European Journal of Cancer, 48(4), 441-446. https://doi.org/10.1016/j.ejca.2011.11.036
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
Steyerberg, E. W. (2019). Clinical prediction models (2nd ed.). Springer. https://doi.org/10.1007/978-3-030-16399-0
Tao, J., Lyu, R., & Cao, X. (2026). A Deep Learning-Based Automated Content Moderation Framework for Online Platforms. Future-Adaptive Intelligence and Lifelong Systems, 1(1).
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Weng, X., Lou, Y., Tao, R., Li, Y., Cao, D., Yu, M., ... & Wang, H. (2021). The association between low birth weight and dental caries among 11-to-13-year-old school age children in Ningbo, China. BMC pediatrics, 21(1), 491.