Abstract
This review examines multimodal evidence across oral surgery, radiation toxicity, and child oral health through an evidence-centered design lens. The analysis asks how heterogeneous clinical evidence should be combined without leakage. 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 population-specific associations being transported across settings. 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 clinical risk review 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).
Chen, P., Yang, K., Safari, M., Peng, J., Guo, B., Qi, P., ... & Scott, J. G. (2026, April). A pilot study of multimodal dosiomics and longitudinal delta-radiomics for predicting radiation-induced xerostomia in head-and-neck cancer. In Proceedings of SPIE--the International Society for Optical Engineering (Vol. 13930, p. 139301P).
Deng, H., Luo, H., Zhu, Y., Li, L., Chen, Z., Zhao, X., Li, M., Zhang, J., Wang, M., Cao, Y., & Kang, Y. (2026). IIB-LPO: Latent policy optimization via iterative information bottleneck. arXiv preprint arXiv:2601.05870.
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.
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.
Ye, M., Lin, X., Liu, W., Calatrava, J., Huang, W., & Wang, H.-L. (2025). Residual ridge height as a potential risk factor for membrane perforation during lateral-window sinus elevation surgery: A systematic review and meta-analysis. BMC Oral Health, 25, 1522.
