Effect of Cranberry Plant Extract on Gene Expression in Antibiotic-Resistant and Biofilm-Forming Staphylococcus aureus and Streptococcus agalactiae Isolated from Vaginal Infections

<ns3:p>Introduction With the rapid advancement in artificial intelligence (AI), its integration into medical fields has gained momentum. AI technologies is increasingly applied for diagnosing and managing temporomandibular joint disorders (TMD), which affect one of the most complex joints in the human body. This review aims to highlight the AI-driven approaches in diagnosing and management of TMD. Methods A comprehensive literature search conducted using PubMed, Web of Science, and Google Scholar, Scholar to identify relevant studies published in English-language studies published since 2007. References from relevant articles were also examined. Results A total of 21 studies met the inclusion criteria, covering diverse AI applications in TMD. These studies included AI-driven medical image segmentation (3), juvenile idiopathic arthritis (3), temporomandibular joint osteoarthritis (TMJOA) detection (4), Temporomandibular joint (TMJ) articular disc displacements and deformities (5), decision support systems (5), and AI-based sound analysis for TMJ assessment (1). The findings suggest that AI, particularly deep learning (DL) and machine learning (ML) techniques, has demonstrated promising accuracy in detecting and classifying TMD and its related pathologies. AI automates segmentation, improves diagnostic consistency, and assists clinicians through predictive modelling. Discussion Despite advancements, challenges remain, as many studies rely on small datasets, limiting generalizability. The absence of standardized evaluation metrics and external validation hinders clinical adoption. Ethical concerns, data privacy issues, and workflow integration pose additional barriers. Conclusion AI is emerging as a valuable tool in TMD diagnosis and management, promising improved accuracy and efficiency, but however, further research involving larger datasets, standardized validation frameworks, and clinically integrated systems is required before widespread clinical implementation can be achieved.</ns3:p>