Responsible Artificial Intelligence and Machine Learning Methods for Equity in Oral Health

About this Research Topic

Background

Due to the increased utilization of digital platforms such as electronic dental records to document patients’ clinical information and imaging (radiographs, CT scans, etc), billions of data have been collected daily. Similarly, people widely adopt social media platforms such as Twitter, Facebook, and Reddit to share about their health conditions and seek recommendations. Finally, social determinants of health data have also been collected digitally through Census as well as electronic health records. These data sources, together, have the potential to provide rich patient information to address health disparities, social determinants, resource utilization, and quality improvement. Moreover, these resources are great sources for developing predictive models, clinical decision support systems, and learning health systems for early diagnosis and prevention. However, despite these excellent opportunities, little to no data have been utilized to address health disparities, develop early diagnosis and preventive strategies. The utilization of machine learning in healthcare has increased significantly; however, it is also important to de-bias machine learning model outputs to ensure fairness. This concept is especially critical when applying these methods.

This Research Topic seeks original research papers, opinions, or reviews addressing the following.

1. Utilize big electronic dental record data, social media data, and/or similar multi-modal datasets for oral health research.
2. Utilize multi-modal datasets and AI methods such as machine learning and deep learning to address oral health disparities.
3. Leverage natural language processing, large language models, and text-mining methods to extract information from dental clinical notes.
4. Develop and test image processing algorithms to extract information from dental radiographs and CT scans to help dentists with accurate diagnoses.
5. Build, test, and validate prediction models for oral diseases.
6. Debias machine learning algorithms to assess fairness and equity in machine learning.
7. Develop clinical decision support systems and learning health systems to enhance preventive care and decision-making at the chair side.
8. Develop health information exchange methods between medical and dental providers to enhance interdisciplinary care.

Keywords: Informatics in Dentistry, Dental Data Science, De-bias Machine Learning Models for Dentistry, Natural Language Processing, Dental Language Processing

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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