Artificial intelligence in diagnosing and treatment of oral mucosal lesions-A systematic review

Abstract

Background: Artificial intelligence (AI) has made deep inroads into dentistry in the last few years. In the modern-day world, artificial intelligence refers to any machine or technology that is able to mimic human cognitive skills like problem solving. To understand AI, it is important to know few of these key aspects. Artificial intelligence is termed as a capability of machines that exhibits a form of its own intelligence. Its aim was to develop machines that can learn through data so that they can solve the problems. Machine learning is part of AI, which depends on algorithms to predict outcomes based on a dataset. The purpose of machine learning is to facilitate machines to learn from data so they can resolve issues without human input. Neural networks are a set of algorithms that compute signals via artificial neurons. The purpose of neural networks is to create neural networks that function like the human brain. Deep learning is a component of machine learning that utilizes the network with different computational layers in a deep neural network to analyse the input data. The purpose of deep learning is to construct a neural network that automatically identifies patterns to improve feature detection. The aim of this systematic review was to analyze whether artificial intelligence is helpful in diagnosis and management of oral mucosal lesions. Aim: This systematic review was to analyze whether artificial intelligence is helpful in diagnosis and management of oral mucosal lesions. Search Methods: Electronic search of the following database was performed: PubMed, Cochrane central register of controlled trials, Google scholar and hand search. Selection Criteria: According to PICO (Population, Intervention, Comparison, Outcome) criteria, the inclusion criteria were worked out. Articles including diagnosis and management of oral mucosal lesion were included.Articles that are related to non-AI areas, articles that are not written in English and articles not related to diagnosis and management of mucosal lesions were excluded. Data Collection And Analysis: We used standard methodological procedures for selection of studies and collecting data. Risk of bias was evaluated and findings were synthesized. Main Results: A total of 3 articles were included in this review that consisted of 3 Case-control studies. Conclusion: Currently there is limited evidence to support application of artificial intelligence in diagnosing and managing oral mucosal lesions. Limited evidence available show artificial intelligence methods comparable to current, conventional diagnostic methods. Large multicentric data is required for integration of these methods into the digital workflow.