Machine Learning-Based Analysis of Electronic Health Records in Dentistry

Abstract

The digital transformation of healthcare has led to the widespread adoption of electronic health records (EHRs), which store valuable patient information. In dentistry, EHRs contain a wealth of data that can be leveraged to improve patient care, treatment outcomes, and practice management. This paper explores the application of machine learning (ML) techniques for analyzing EHRs in dentistry. Specifically, it discusses the challenges and opportunities of using ML to extract meaningful insights from EHRs, such as predicting oral health outcomes, identifying risk factors, and improving treatment planning. The paper also addresses the ethical considerations and privacy concerns associated with the use of EHR data in ML models. Overall, this study highlights the potential of ML-based EHR analysis to enhance dental care delivery and inform decision-making processes in dentistry