Evidence-based medicine (EBM) enhances clinical decision-making but faces implementation challenges, particularly in dentistry, where patient-specific complexities limit its effectiveness. This article examines EBM through the lens of Aristotelian logic, exploring its use of deductive and inductive reasoning and its limitations in addressing real-world variability. We then discuss how artificial intelligence (AI) can enhance EBM by synthesizing data, automating evidence appraisal, and generating personalized treatment insights. While AI offers a promising solution, it also presents challenges related to ethics, transparency, and reliability. Integrating AI into EBM requires careful consideration to ensure precise, adaptive, and patient-centered decision-making.Knowledge Transfer Statement:This commentary provides a critical discourse on the challenges of evidence-based medicine and how artificial intelligence could help address these shortcomings.
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