Artificial Intelligence Innovations in Understanding Oral Submucous Fibrosis: A Nouvelle Modernistic Approach: A Systematic Review

Abstract

Background: Oral submucous fibrosis (OSMF) is a common precancerous condition prevalent in the South Asian continent. An immediate diagnosis and identification of this lesion is essential to prevent its malignant transformation. Artificial intelligence can analyze data, patient records, and clinical photographs to finalize a treatment plan in oral submucous fibrosis patients by the oral pathologist and clinicians' intervention. The current study aimed to establish the key role of machine learning software in diagnostic intervention and management of oral submucous fibrosis in the mass population with ease for most health professionals. Material & Methods: We searched relevant databases like PubMed, Scopus, Web of Science, Cochrane, and Google Scholar using keywords like artificial intelligence, machine learning, treatment planning, diagnosis, oral submucous fibrosis, image analysis, predictive modeling, and deep neural networks. Results: Nine studies were selected after all scrutiny, with suggested outcomes under AI that can be utilized to understand oral submucous fibrosis intervention. Machine learning is a helpful tool in fabricating image prediction models under neural networks. This can be an important diagnostic aid for oral health professionals. Conclusions: In the field of oral pathologies, AI has the potential to enhance diagnostic accuracy and patient care, improve diagnostic outcomes, and improve the prognosis of the disease. However, the development and application of AI in oral healthcare, especially for oral submucous fibrosis patients, is a new frontier for modernistic approaches for future treatment amenities.