Author:
Mouradian Wendy,Lee Janice,Wilentz Joan,Somerman Martha
Abstract
The past decade has seen marked increases in research findings identifying oral-systemic links. Yet, much of dental research remains poorly integrated with mainstream biomedical research. The historic separation of dentistry from medicine has led to siloed approaches in education, research and practice, ultimately depriving patients, providers, and policy makers of findings that could benefit overall health and well-being. These omissions amount to lost opportunities for risk assessment, diagnosis, early intervention and prevention of disease, increasing cost and contributing to a fragmented and inefficient healthcare delivery system. This perspective provides examples where fostering interprofessional research collaborations has advanced scientific understanding and yielded clinical benefits. In contrast are examples where failure to include dental research findings has limited progress and led to adverse health outcomes. The impetus to overcome the dental-medical research divide gains further urgency today in light of the coronavirus pandemic where contributions that dental research can make to understanding the pathophysiology of the SARS-CoV-2 virus and in diagnosing and preventing infection are described. Eliminating the research divide will require collaborative and trans-disciplinary research to ensure incorporation of dental research findings in broad areas of biomedical research. Enhanced communication, including interoperable dental/medical electronic health records and educational efforts will be needed so that the public, health care providers, researchers, professional schools, organizations, and policymakers can fully utilize oral health scientific information to meet the overall health needs of the public.
Reference98 articles.
1. On the dangers of stochastic parrots: can language models be too big?;Bender;FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency,2021
2. MathenyM
ThadaneyIsrani S
AhmedM
WhicherD
Washington, DCNational Academy of MedicineArtificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril2019
3. Development and validation of novel three-dimensional craniofacial landmarks on cone-beam computed tomography scans;Liberton;J Craniofac Surg.,2019
4. Deep geodesic learning for segmentation and anatomical landmarking;Torosdagli;IEEE Trans Med Imaging.,2019
5. Malocclusion classification on 3d cone-beam CT craniofacial images using multi-channel deep learning models;Kim;Annu Int Conf IEEE Eng Med Biol Soc.,2020
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