# Machine Learning for Child Oral Health: A Scoping Review
## 4 Discussion
In children, they also allow the dentist to evaluate if the teeth and jaw bones are developing properly and to address any concerns early on [62]. Neural networks can be used to identify all of these changes, such as caries progression, with more accuracy and efficiency than that of the clinician. The thorough analysis of these images by clinician is time consuming and laborious leading to the possibility of some conditions going undiagnosed. Therefore, ML techniques such as neural networks were widely used in the articles reviewed in our paper. The implications of this study underscore the urgent need for increased cooperation in research regarding ML applications for pediatric dentistry. The observed disparity in contributions from LHICs raises concerns regarding their knowledge and capability to progress in this field. Efforts should be directed toward overcoming barriers faced by LMICs, including limited research funding for comprehensive landscape exploration and the need for improved access to training and resources. Furthermore, establishing partnerships between HICs and LMICs could foster knowledge exchange and capacity building, enhancing local expertise. Additionally, the prevalence of neural networks in dental diagnostics suggests the importance of leveraging image recognition technologies for more efficient and precise patient examinations. These technologies can not only streamline diagnostic processes but also enhance the accuracy of treatment planning, ultimately improving patient outcomes. Addressing these implications could contribute to seamless integration of ML in pediatric dentistry, ensuring that advancements benefit populations worldwide and help bridge the gap in healthcare disparities. By prioritizing collaboration and resource sharing, the global dental community can work toward a future where innovative technologies are accessible to all, enhancing care for children everywhere. The study has certain limitations. Despite conducting an extensive literature search across multiple databases, the possibility of missing relevant articles cannot be entirely ruled out. This limitation highlights the inherent challenges in ensuring comprehensive coverage in scoping review. Furthermore, the deliberate narrowing of the study's scope based on age limitations restricts the inclusion of valuable information available in the broader literature, potentially overlooking important trends and insights that could inform future research. Additionally, the exclusion of non-English language publications introduces a potential bias, as the findings may not capture diverse perspectives from regions where English is not the primary language of scientific discourse. This language-based exclusion criterion may hinder the ability to understand fully the global landscape of research in this field. By omitting insights from research conducted in countries and areas where English is not commonly used in academic discourse, the study risks underrepresenting important cultural and contextual factors that could influence research outcomes and practices. These limitations highlight the need for future studies to adopt a more inclusive approach, potentially incorporating multi-language searches and broader inclusion criteria to ensure a more comprehensive understanding of the subject matter. By addressing these gaps, subsequent research can enhance the richness of the data and provide a more holistic view of the trends and challenges within the field.