Objectives: This review aims to explore the applications of artificial intelligence (AI) in prosthodontics and implant dentistry, focusing on its performance outcomes and associated ethical concerns. Materials and Methods: Following the PRISMA guidelines, a search was conducted across databases such as PubMed, Medline, Web of Science, and Scopus. Studies published between January 2022 and May 2024, in English, were considered. The Population (P) included patients or extracted teeth with AI applications in prosthodontics and implant dentistry; the Intervention (I) was AI-based tools; the Comparison (C) was traditional methods, and the Outcome (O) involved AI performance outcomes and ethical considerations. The Newcastle–Ottawa Scale was used to assess the quality and risk of bias in the studies. Results: Out of 3420 initially identified articles, 18 met the inclusion criteria for AI applications in prosthodontics and implant dentistry. The review highlighted AI’s significant role in improving diagnostic accuracy, treatment planning, and prosthesis design. AI models demonstrated high accuracy in classifying dental implants and predicting implant outcomes, although limitations were noted in data diversity and model generalizability. Regarding ethical issues, five studies identified concerns such as data privacy, system bias, and the potential replacement of human roles by AI. While patients generally viewed AI positively, dental professionals expressed hesitancy due to a lack of familiarity and regulatory guidelines, highlighting the need for better education and ethical frameworks. Conclusions: AI has the potential to revolutionize prosthodontics and implant dentistry by enhancing treatment accuracy and efficiency. However, there is a pressing need to address ethical issues through comprehensive training and the development of regulatory frameworks. Future research should focus on broadening AI applications and addressing the identified ethical concerns.

Aim(s): This cross‐sectional clinical study aimed to explore the untargeted metabolomic and lipidomic profiles in gingival crevicular fluid (GCF) obtained from healthy individuals and patients with periodontitis. Materials and Methods: GCF was collected from 17 periodontally healthy and 19 periodontitis patients. For the unbiased characterisation of positively and negatively charged polar metabolite and lipid compounds, we employed hydrophilic interaction and reversed‐phase liquid chromatography/mass spectrometry platforms, followed by a semisupervised deep learning‐based approach for metabolomic peak curation and data analysis. Results: A total of 256 metabolites were identified with Metabolomics Standards Initiative (MSI) confidence Levels 1–3, including 198 elevated and 58 diminished compounds in periodontal lesions ( q < 0.05, |log ₂ ‐transformed Fold Change (FC)| > 1). Periodontitis samples exhibited a significant positive accumulation of purine degradation and ceramide metabolites, along with a negative regulation of oxy fatty acids metabolism. Additionally, we observed a significant increase in established periodontitis biomarkers, including N‐acetylneuraminic acid, citrulline and 2‐pyrrolidineacetic acid. The study also characterised distinct differences in bacterial and fungal metabolite profiles between the healthy and diseased samples. Conclusions: These findings suggest that untargeted metabolomic screening of GCF may significantly improve our understanding of biochemical changes between healthy tissue and periodontitis. This knowledge is pivotal for the development of a precision paradigm in periodontitis.