Personalized Oral and Dental Care

Amato, Alessandra

doi:10.3390/jpm13010110

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Integrating explainability into AI within oral healthcare will boost trust of healthcare providers and their patients, enhancing confidence in the institution ( 49 ). Those explainable and interpretable AI techniques, aimed at augmenting rather than replacing oral health practitioners' judgment and skills, have the potential to achieve personalized dental and oral care that is unbiased, equitable, and transparent ( 50 ).…”

Section: Responsible Ai For Addressing Inequity/bias In Oral Healthcarementioning

confidence: 99%

Responsible artificial intelligence for addressing equity in oral healthcare

Khoury,

Ferguson,

Price

et al. 2024

Front. Oral. Health

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Oral diseases pose a significant burden on global healthcare. While many oral conditions are preventable and manageable through regular dental office visits, a substantial portion of the population faces obstacles in accessing essential and affordable quality oral healthcare. In this mini review, we describe the issue of inequity and bias in oral healthcare and discuss various strategies to address these challenges, with an emphasis on the application of artificial intelligence (AI). Recent advances in AI technologies have led to significant performance improvements in oral healthcare. AI also holds tremendous potential for advancing equity in oral healthcare, yet its application must be approached with caution to prevent the exacerbation of inequities. The “black box” approaches of some advanced AI models raise uncertainty about their operations and decision-making processes. To this end, we discuss the use of interpretable and explainable AI techniques in enhancing transparency and trustworthiness. Those techniques, aimed at augmenting rather than replacing oral health practitioners’ judgment and skills, have the potential to achieve personalized dental and oral care that is unbiased, equitable, and transparent. Overall, achieving equity in oral healthcare through the responsible use of AI requires collective efforts from all stakeholders involved in the design, implementation, regulation, and utilization of AI systems. We use the United States as an example due to its uniquely diverse population, making it an excellent model for our discussion. However, the general and responsible AI strategies suggested in this article can be applied to address equity in oral healthcare on a global level.

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Section: Responsible Ai For Addressing Inequity/bias In Oral Healthcarementioning

confidence: 99%

Responsible artificial intelligence for addressing equity in oral healthcare

Khoury,

Ferguson,

Price

et al. 2024

Front. Oral. Health

View full text Add to dashboard Cite

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Salivary Biomarkers for Oral Cancer Detection: Insights from Human DNA and RNA Analysis

Navale,

Deshpande

2024

CHAMC

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Oral cancer is a significant global health concern, with a high mortality rate mainly due to late-stage diagnosis. Early detection plays a critical role in improving patient outcomes, highlighting the need for non-invasive and accessible screening methods. Salivary biomarkers have emerged as a promising avenue for oral cancer detection, leveraging advancements in human DNA and RNA analysis. Several DNA-based biomarkers, such as genetic mutations, chromosomal aberrations, and epigenetic alterations, have shown promise in detecting oral cancer at various stages. Likewise, RNA-based biomarkers, including microRNAs, long non-coding RNAs, and messenger RNAs, have demonstrated potential for diagnosing oral cancer and predicting treatment outcomes. The integration of high-throughput sequencing technologies, such as next-generation sequencing and transcriptomic profiling, has enabled the identification of novel biomarkers and provided deeper insights into the molecular mechanisms underlying oral cancer development and progression. Despite the promising results, challenges remain in standardizing sample collection, establishing robust biomarker panels, and validating their clinical utility. Nevertheless, salivary biomarkers hold great promise as a non-invasive, cost-effective, and accessible approach for oral cancer detection, ultimately leading to improved patient outcomes through early diagnosis and intervention. The analysis of genetic material obtained from saliva offers several advantages, including ease of collection, non-invasiveness, and the potential for repeated sampling. Furthermore, saliva reflects the physiological and pathological status of the oral cavity, making it an ideal source for biomarker discovery and validation. This article presents a comprehensive review of the current research on salivary biomarkers for oral cancer detection, focusing on insights gained from human DNA and RNA analysis.

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Personalized Oral and Dental Care

Abstract: Recent advances in genomics, data analytics technologies, and biotechnology have been unprecedented, ushering in a new era of healthcare in which interventions are increasingly tailored to individual patients [...]

Cited by 2 publications

References 23 publications

Responsible artificial intelligence for addressing equity in oral healthcare

Responsible artificial intelligence for addressing equity in oral healthcare

Salivary Biomarkers for Oral Cancer Detection: Insights from Human DNA and RNA Analysis

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