Evaluating the Effectiveness of AI in Enhancing Early Detection of Dental and Oral Diseases

Abstract

Detecting dental and oral diseases early is important to avoid serious long-term health problems and the need for painful, expensive and lengthy treatments later on. Dental issues such as cavities, gum disease, mouth sores, and oral cancer often begin with subtle, small changes which the patient might not even notice. In fact, it may also be difficult for a dentist to detect them during routine dental checkups. These early signs may appear in X-rays or images, but may be missed especially when symptoms are mild and the person studying the X-rays is inexperienced. In recent years artificial intelligence (AI) and machine-learning based tools have been developed to analyse dental X-rays and mouth images to help with early detection and diagnosis. This paper examines whether AI-based tools, when used along with regular dental checkups, can improve the early detection rates of dental and oral diseases by making diagnosis consistent and therefore more reliable. Using a literature-based case study methodology, the study reviews existing academic research, clinical studies, and real-world applications of AI systems in dentistry. The case studies focus on AI-assisted detection of dental cavities, gum disease, mouth lesions, and early-stage oral cancer. Overall the study shows that AI-based tools in the oral and dental field are often able to accurately identify diseases at their early stages, with accuracy rates similar to and in some cases better than dentists, particularly when they deal with images. However, several limitations and challenges are also seen. These include the fact that AI-based tools need high quality images, the possibility that the data used to develop them may have bias, unclear decision making processes and ethical concerns regarding over-reliance on these tools to the exclusion of human judgment. In the end, the study suggests that AI tools in this field can make a very useful impact in early detection, creating standard diagnostic practices and leading to better results for patients. However the need for human supervision cannot be done away with and these tools are best suited for a supporting role.