The use of artificial intelligence in the diagnosis of odontogenic cysts and tumors

The application of artificial intelligence (AI) in healthcare has garnered growing interest, particularly for its ability to improve diagnostic accuracy and streamline clinical workflows. This literature review examines the latest advancements and ongoing challenges in the use of AI for diagnosing odontogenic cysts and tumors. These lesions, originating from odontogenic epithelium or ectomesenchyme, present with a wide range of clinical and radiographic features that often overlap, complicating accurate diagnosis. AI, particularly through machine learning (ML) and deep learning (DL) models, offers promising solutions to these challenges by enhancing automation and precision in diagnostic processes. Numerous studies have highlighted the potential of AI algorithms to analyze various imaging modalities, such as radiographs, computed tomography (CT), and histopathological slides, achieving diagnostic outcomes comparable to those of expert clinicians. These AI systems have been designed to identify key radiological and histopathological characteristics, enabling earlier and more accurate detection of odontogenic lesions. Despite these promising results, significant challenges persist, such as the need for larger, more diverse datasets, the establishment of standardized protocols, and the seamless integration of AI tools into existing clinical practices.