International endodontic journal · 2026
The Fundamental Necessities of Comprehensive Dental Repositories to Support the Generalisation of AI Applications in Endodontics.
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Thakkar Shilpa, Flores Mario, Singh Brij, Khan Asma
Аннотация
AIM: Artificial intelligence (AI) has the potential to reshape the healthcare industry, particularly in disease detection, diagnosis and management, as well as in supporting clinical decision-making to enhance patient care. However, realising the full potential of AI in healthcare depends on several interrelated factors, including data quality, interoperability and ethical governance, which are examined in this review.
METHODOLOGY: Clinical data repositories, such as radiological and histopathological image databases, clinical trial datasets, multi-omics data and comprehensive electronic health records, serve as the foundation for developing, validating and deploying machine learning (ML) models aimed at improving patient outcomes. Issues such as data quality, completeness, and representativeness of training data needed for model accuracy and reliability across diverse populations and clinical settings are also highlighted.
RESULTS AND CONCLUSION: This review discusses ML methodologies and protocols that facilitate the integration of heterogeneous healthcare datasets while maintaining data privacy, security and transparency. These approaches address key technical, regulatory and ethical considerations essential for responsible AI implementation. Additionally, the review highlights AI and ML models applicable to endodontic procedures and treatments, emphasising their potential to enhance diagnostic precision, optimise treatment planning and support the development of effective clinical tools.
METHODOLOGY: Clinical data repositories, such as radiological and histopathological image databases, clinical trial datasets, multi-omics data and comprehensive electronic health records, serve as the foundation for developing, validating and deploying machine learning (ML) models aimed at improving patient outcomes. Issues such as data quality, completeness, and representativeness of training data needed for model accuracy and reliability across diverse populations and clinical settings are also highlighted.
RESULTS AND CONCLUSION: This review discusses ML methodologies and protocols that facilitate the integration of heterogeneous healthcare datasets while maintaining data privacy, security and transparency. These approaches address key technical, regulatory and ethical considerations essential for responsible AI implementation. Additionally, the review highlights AI and ML models applicable to endodontic procedures and treatments, emphasising their potential to enhance diagnostic precision, optimise treatment planning and support the development of effective clinical tools.