Sir, we write to expand upon the valuable insights presented in the recent article by Batool et al. concerning the application of GPT models in post-operative dental care.1 Building upon this foundation, I propose using advanced artificial intelligence (AI) models, encompassing Large Language Models (LLMs) such as ChatGPT, to predict future oral diseases. Integrating these with customer relationship management (CRM) software can enhance preventative care.
The integration of advanced AI models, such as Foresight, DP-GAT, and LLMs, has shown remarkable capabilities in disease prediction across diverse medical fields. Foresight uses transformer-based techniques to forecast medical events from various data sources, while DP-GAT employs graph attention networks for accurate disease progression prediction.2,3 Additionally, machine learning models have significantly improved the accuracy of diagnostic and prognostic assessments, exemplified by the ORIENTATE software, which accurately predicts the need for a second sedation in children with special healthcare needs.4
Recent studies have established correlations between specific blood markers and oral health conditions, offering valuable insights for early detection and intervention. Elevated levels of C-reactive protein (CRP) and Interleukin-6 (IL-6) have been associated with periodontal disease.5 Similarly, high blood glucose and HbA1c levels indicate a heightened risk of periodontal infections in diabetic patients.6 The integration of these markers into predictive models can further refine the accuracy of forecasts for oral diseases.
The integration of advanced predictive models in healthcare offers numerous benefits. Early detection allows timely intervention, personalised care, and continuous patient monitoring. These models also have the potential to bolster public health initiatives, improve resource allocation, and reduce the burden of disease. It is crucial to prioritise patient privacy and data protection when integrating predictive models into CRM software.
Further research into integrating AI-powered CRM systems with predictive models holds immense promise for revolutionising preventive dentistry and improving oral health outcomes.
References
Batool I, Naved N, Kazmi S M R, Umer F. Leveraging Large Language Models in the delivery of post-operative dental care: a comparison between an embedded GPT model and ChatGPT. BDJ Open 2024; doi: 10.1038/s41405-024-00226-3.
Kraljevic Z, Bean D, Shek A et al. Foresight -- Generative Pretrained Transformer (GPT) for Modelling of Patient Timelines using EHRs. 2022. Available at: https://arxiv.org/abs/2212.08072 (accessed August 2024).
Foo A, Hsu W, Lee M, Tan G. DP-GAT: A Framework for Image-based Disease Progression Prediction. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022. Available at: https://consensus.app/papers/dpgat-framework-imagebased-disease-progression-foo/932f4cb8fb8b538aa479e2d8d7d22413/ (accessed August 2024).
Gomez-Rios I, Egea-Lopez E, Ruiz A J O. ORIENTATE: automated machine learning classifiers for oral health prediction and research. BMC Oral Health 2023; doi: 10.1186/s12903-023-03112-w.
Cruz-Ávila J, Hernández-Pérez E, González-González R, Bologna-Molina R, Molina-Frechero N. Periodontal disease in obese patients; Interleukin-6 and C-Reactive protein study: A systematic review. Dent J (Basel) 2022; doi: 10.3390/dj10120225.
Rahim A, Hassan S, Ullah N et al. Association and comparison of periodontal and oral hygiene status with serum HbA1c levels: a cross-sectional study. BMC Oral Health 2023; doi: 10.1186/s12903-023-03042-7.
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Krishna, S., Vyas, A. Leveraging AI and CRM integration for predictive oral health care. Br Dent J 237, 240 (2024). https://doi.org/10.1038/s41415-024-7796-7
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DOI: https://doi.org/10.1038/s41415-024-7796-7
