AI-Driven Patient Engagement Ecosystems: Advancing Predictive Analytics and Personalised Care Through Multimodal Dental Data
Keywords:
artificial intelligence, dentistry, multimodal data, patient engagement, teledentistry
Abstract
Fragmented dental information flows hinder the early detection of oral diseases and limit the personalisation of care, despite rapid progress in artificial intelligence and telehealth. The article outlines an AI-driven patient engagement ecosystem, grounded in multimodal dental data, that integrates imaging, electronic health records, demographic information, and patient-generated data from mobile and teledentistry channels. The aim is to align multimodal predictive analytics with continuous, personalised engagement at both individual and population levels. The study relies on an analytical synthesis of recent work on dental AI, public health–oriented predictive models, teledentistry platforms, and multimodal machine learning architectures. Dental AI Engagement Ecosystem (DAIE) connects curated dental datasets and fusion models with patient-facing services, including remote triage, conversational agents, risk-based recalls, and tailored behavioural support. Special attention is paid to vulnerable populations, the governance of data flows, and the explainability of predictions across clinical and home settings. The framework provides methodological and practical guidance for clinicians, health system managers, and digital health developers designing AI-ready dental care pathways.
References
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[23] Aboalshamat, K. T., Alnafei, J. H., Alkhattabi, L. A., Alhawsawi, G. Y., Alahmadi, S. M., Almalki, S. O., & Nassar, A. A. (2025). Can ChatGPT be trusted? Evaluating AI responses to oral health questions among pregnant Arabic-speaking women. BMC Oral Health, 25(1), 1597. doi:10.1186/s12903-025-06909-z
[24] Umakanthan, S., Sahu, P., Ranade, A., Bukelo, M., Rao, J., Abrahao-Machado, L., Dahal, S., Kumar, H., & Kv, D. (2020). Origin, transmission, diagnosis and management of coronavirus disease 2019 (COVID-19). Postgraduate Medical Journal, 96(1142), 753–758. doi:10.1136/postgradmedj-2020-138234
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[2] Bamashmous, M. (2025). The role of artificial intelligence in transforming dental public health: Current applications, ethical considerations, and future directions. The Open Dentistry Journal, 19, e18742106363413. http://dx.doi.org/10.2174/0118742106363413250211053942
[3] Danesh, D. O., Bullock, A. D., McGrath, C., & Wong, H. M. (2025). Editorial: Use of teledentistry and artificial intelligence to enhance dental care for vulnerable populations. Frontiers in Dental Medicine, 6, 1723059. https://doi.org/10.3389/fdmed.2025.1723059
[4] Erol, I., Peker, I., Medeni, I. T., & Yuce, F. (2025). Towards precision dentistry through artificial intelligence and blockchain-based digital twins: Investigating challenges and solution strategies. Technology in Society, 83, 103051. https://doi.org/10.1016/j.techsoc.2025.103051
[5] Huang, Y., Liu, W., Yao, C., Miao, X., Guan, X., Lu, X., Liang, X., Ma, L., Tang, S., Zhang, Z., & Zhan, J. (2024). A multimodal dental dataset facilitating machine learning research and clinic services. Scientific Data, 11, 1291. https://doi.org/10.1038/s41597-024-04130-1
[6] Kaushik, R., & Rapaka, R. (2025). AI-driven evolution in teledentistry: A comprehensive overview of technology and clinical applications. Dentistry Review, 5(2), 100154. https://doi.org/10.1016/j.dentre.2025.100154
[7] Krones, F. H., Marikkar, U., Parsons, G., Szmul, A., & Mahdi, A. (2024). Review of multimodal machine learning approaches in healthcare. Information Fusion, 114, 102690. https://doi.org/10.1016/j.inffus.2024.102690
[8] Liu, T.-Y., Lee, K.-H., Mukundan, A., Karmakar, R., Dhiman, H., & Wang, H.-C. (2025). AI in dentistry: Innovations, ethical considerations, and integration barriers. Bioengineering, 12(9), 928. https://doi.org/10.3390/bioengineering12090928
[9] Surdu, A., Foia, C. I., Luchian, I., Trifan, D., Budala, D. G., Scutariu, M. M., Ciupilan, C., Puha, B., & Tatarciuc, D. (2025). Telemedicine and digital tools in dentistry: Enhancing diagnosis and remote patient care. Medicina, 61(5), 826. https://doi.org/10.3390/medicina61050826
[10] Wang, Y., Ye, F., Chen, Y., Wang, C., Wu, C., Xu, F., Ma, Z., Liu, Y., Zhang, Y., Cao, M., & Chen, X. (2025). A multi-modal dental dataset for semi-supervised deep learning image segmentation. Scientific Data, 12(1), 117. https://doi.org/10.1038/s41597-024-04306-9
[11] Shamshad, F., Khan, S., Zamir, S. W., Khan, M. H., Hayat, M., Khan, F. S., & Fu, H. (2023). Transformers in medical imaging: A survey. Medical Image Analysis, 88, 102802.
[12] Siam, M. K., Hossain Faruk, M. J., He, B., Cheng, J. Q., & Gu, H. (2025). Multimodal models in healthcare: Methods, challenges, and future directions for enhanced clinical decision support. Information, 16(11), 971. https://doi.org/10.3390/info16110971
[13] Nikam, D., & Singhal, A. (2024). A review on machine learning and deep learning approaches for predictive healthcare analytics. Journal of Electrical Systems, 20(11s), 2072–2080. https://doi.org/10.52783/jes.7697
[14] Mohammad-Rahimi, H., Motamedian, S. R., Rohban, M. H., Krois, J., Uribe, S. E., Mahmoudinia, E., Rokhshad, R., Nadimi, M., & Schwendicke, F. (2022). Deep learning for caries detection: A systematic review. Journal of Dentistry, 122, 104115. doi:10.1016/j.jdent.2022.104115
[15] Rahman, N., Nathwani, S., & Kandiah, T. (2020). Teledentistry from a patient perspective during the coronavirus pandemic. British Dental Journal, Advance online publication, 1–4. doi:10.1038/s41415-020-1919-6
[16] Hung, M., Lipsky, M. S., Phuatrakoon, T. N., Nguyen, M., Licari, F. W., & Unni, E. J. (2022). Teledentistry implementation during the COVID-19 pandemic: Scoping review. Interactive Journal of Medical Research, 11(2), e39955. doi:10.2196/39955
[17] Hung, M., Licari, F. W., Hon, E. S., Lauren, E., Su, S., Birmingham, W. C., Wadsworth, L. L., Lassetter, J. H., Graff, T. C., Harman, W., Carroll, W. B., & Lipsky, M. S. (2021). In an era of uncertainty: Impact of COVID-19 on dental education. Journal of Dental Education, 85(2), 148–156. doi:10.1002/jdd.12404
[18] Khan, S. A., & Omar, H. (2013). Teledentistry in practice: Literature review. Telemedicine and e-Health, 19(7), 565–567. doi:10.1089/tmj.2012.0200
[19] Alabdullah, J. H., & Daniel, S. J. (2018). A systematic review on the validity of teledentistry. Telemedicine and e-Health, 24(8), 639–648. doi:10.1089/tmj.2017.0132
[20] Crawford, E., & Taylor, N. (2020). The effective use of an e-dentistry service during the COVID-19 crisis. Journal of Orthodontics, 47(4), 330–337. doi:10.1177/1465312520949557
[21] Peng, X., Xu, X., Li, Y., Cheng, L., Zhou, X., & Ren, B. (2020). Transmission routes of 2019-nCoV and controls in dental practice. International Journal of Oral Science, 12, 9. doi:10.1038/s41368-020-0075-9
[22] Jampani, N. D., Nutalapati, R., Dontula, B. S. K., & Boyapati, R. (2011). Applications of teledentistry: A literature review and update. Journal of International Society of Preventive & Community Dentistry, 1(2), 37–44.
[23] Aboalshamat, K. T., Alnafei, J. H., Alkhattabi, L. A., Alhawsawi, G. Y., Alahmadi, S. M., Almalki, S. O., & Nassar, A. A. (2025). Can ChatGPT be trusted? Evaluating AI responses to oral health questions among pregnant Arabic-speaking women. BMC Oral Health, 25(1), 1597. doi:10.1186/s12903-025-06909-z
[24] Umakanthan, S., Sahu, P., Ranade, A., Bukelo, M., Rao, J., Abrahao-Machado, L., Dahal, S., Kumar, H., & Kv, D. (2020). Origin, transmission, diagnosis and management of coronavirus disease 2019 (COVID-19). Postgraduate Medical Journal, 96(1142), 753–758. doi:10.1136/postgradmedj-2020-138234
[25] Heymann, D. L., & Shindo, N. (2020). COVID-19: What is next for public health? The Lancet, 395(10224), 542–545. doi:10.1016/S0140-6736(20)30374-3
[26] World Health Organization. (2020). WHO Director-General’s opening remarks at the media briefing on COVID-19. World Health Organization. https://reliefweb.int/report/world/who-director-generals-opening-remarks-media-briefing-covid-19-4-december-2020
[27] NHS England & NHS Improvement. (2020). COVID-19 guidance and standard operating procedure: Urgent dental care systems in the context of coronavirus delay phase. NHS England. https://www.england.nhs.uk/coronavirus/wp-content/uploads/sites/52/2020/06/C0581-covid-19-urgent-dental-care-sop-update-16-june-20-.pdf
[28] Centers for Disease Control and Prevention. (2022). Guidance for Institutions of Higher Education (IHEs). CDC. https://stacks.cdc.gov/view/cdc/108282
[29] Liang, Y., Fan, H.-W., Fang, Z., Miao, L., Li, W., Zhang, X., Sun, W., Wang, K., He, L., & Chen, X. A. (2020). OralCam: Enabling self-examination and awareness of oral health using a smartphone camera (arXiv:2001.05621). arXiv.
[30] Liang, Y., Qiu, L., Lu, T., Fang, Z., Tu, D., Yang, J., Zhao, T., Shao, Y., Wang, K., Chen, X. A., & He, L. (2021). OralViewer: 3D demonstration of dental surgeries for patient education with oral cavity reconstruction from a 2D panoramic X-ray (arXiv:2101.00098). arXiv.
[31] Farhadi Nia, M. (2024). Transforming dental diagnostics with artificial intelligence: Advanced integration of ChatGPT and large language models for patient care (arXiv:2406.06616). arXiv.
[32] Rocca, M. A., Kudryk, V. L., Pajak, J. C., & Morris, T. (1999). The evolution of a teledentistry system within the Department of Defense. In Proceedings of the AMIA Symposium (pp. 921–924).
Published
2026-07-13
How to Cite
Tariq, U. (2026). AI-Driven Patient Engagement Ecosystems: Advancing Predictive Analytics and Personalised Care Through Multimodal Dental Data. European Journal of Science, Innovation and Technology, 6(4), 8-22. Retrieved from https://www.ejsit-journal.com/index.php/ejsit/article/view/778
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