The increasing collection of health data coupled with continuous improvements in information processing and analysis have enabled precision medicine. Traditionally, dentistry has lagged behind medicine in the adoption and seamless integration of new technologies, so what is the status quo of precision dentistry and data science that can improve oral health outcomes?
Big Data has played an essential role in the progression of medicine in the 21st century. Digital health data is collected specifically, in the context of examinations and treatments by healthcare providers, but also non-specifically through personal health apps, social media, and other devices. Due to the rapid progress in information technology, completely new approaches in dental medicine are feasible today. A key technology in the future of healthcare is artificial intelligence (AI) with the disruptive potential to influence all dental disciplines.
The objective of this Research Topic is to provide an update on the current knowledge with state-of-the-art theory and practical information on precision dentistry and e-health data science in oral healthcare focusing on (1) telemedicine; (2) digital therapeutics; and (3) care navigation.
Emphasis is placed on identifying future research needs to harness the power of data to improve oral health outcomes. So far, the continuous increase in digital health data has not resulted in dramatic improvements in diagnostic and treatment as we have not yet accomplished their clinical translation. Overcoming hype and expert opinions, this Research Topic aims to shed more light on the field of AI in dentistry and generate evidence on how advances in precision dentistry and data science can help:
(1) General practitioners to improve the oral health of their patients; and
(2) Researchers to generate evidence for the development of clinical guidelines that do rely on data instead of on expert opinions.
Further, this Research Topic aims to provide a deeper understanding of the areas of machine learning (ML), natural language processing (NLP), and convolutional neural networks (CNN) and their impact on society, including ethical concerns.
The overall scope is to identify new insights in dental medicine and oral health using large datasets and advanced analytic techniques with a clinical, epidemiological, and public health perspective. This Research Topic welcomes original research articles presenting clinical and laboratory trials, narrative and systematic reviews following the PRISMA guidelines, as well as tutorial-type papers and communication articles considering the perspectives of the various stakeholders with regard to precision dentistry in dental medicine:
• Telemedicine and remote patient monitoring
• Digital therapeutics for treatment optimization in all fields of dentistry
• Care navigation for (self-) triage of patients
• Robotic dentistry for precision treatment
• Data collection and processing in oral health
• Interoperability of electronic health records
• Use of AI and ML applications in dentistry, such as CNN and NLP
• Predictive modeling
• Evaluation methods of digital tools and applications
• Evidence synthesis for policy, clinical decision-making, and the public (health literacy)
The increasing collection of health data coupled with continuous improvements in information processing and analysis have enabled precision medicine. Traditionally, dentistry has lagged behind medicine in the adoption and seamless integration of new technologies, so what is the status quo of precision dentistry and data science that can improve oral health outcomes?
Big Data has played an essential role in the progression of medicine in the 21st century. Digital health data is collected specifically, in the context of examinations and treatments by healthcare providers, but also non-specifically through personal health apps, social media, and other devices. Due to the rapid progress in information technology, completely new approaches in dental medicine are feasible today. A key technology in the future of healthcare is artificial intelligence (AI) with the disruptive potential to influence all dental disciplines.
The objective of this Research Topic is to provide an update on the current knowledge with state-of-the-art theory and practical information on precision dentistry and e-health data science in oral healthcare focusing on (1) telemedicine; (2) digital therapeutics; and (3) care navigation.
Emphasis is placed on identifying future research needs to harness the power of data to improve oral health outcomes. So far, the continuous increase in digital health data has not resulted in dramatic improvements in diagnostic and treatment as we have not yet accomplished their clinical translation. Overcoming hype and expert opinions, this Research Topic aims to shed more light on the field of AI in dentistry and generate evidence on how advances in precision dentistry and data science can help:
(1) General practitioners to improve the oral health of their patients; and
(2) Researchers to generate evidence for the development of clinical guidelines that do rely on data instead of on expert opinions.
Further, this Research Topic aims to provide a deeper understanding of the areas of machine learning (ML), natural language processing (NLP), and convolutional neural networks (CNN) and their impact on society, including ethical concerns.
The overall scope is to identify new insights in dental medicine and oral health using large datasets and advanced analytic techniques with a clinical, epidemiological, and public health perspective. This Research Topic welcomes original research articles presenting clinical and laboratory trials, narrative and systematic reviews following the PRISMA guidelines, as well as tutorial-type papers and communication articles considering the perspectives of the various stakeholders with regard to precision dentistry in dental medicine:
• Telemedicine and remote patient monitoring
• Digital therapeutics for treatment optimization in all fields of dentistry
• Care navigation for (self-) triage of patients
• Robotic dentistry for precision treatment
• Data collection and processing in oral health
• Interoperability of electronic health records
• Use of AI and ML applications in dentistry, such as CNN and NLP
• Predictive modeling
• Evaluation methods of digital tools and applications
• Evidence synthesis for policy, clinical decision-making, and the public (health literacy)