Artificial intelligence is transforming pathology by enhancing diagnostic precision and supporting clinical decision-making. Its application often involves digital imaging and machine learning to analyse whole slide imaging (WSI) data. AI algorithms can detect cellular abnormalities, classify cancers, and predict outcomes more accurately than traditional approaches, enabling faster and more reliable diagnoses. This is especially valuable in cancer pathology, where early detection and precise classification are critical for effective treatment. Despite its benefits, AI faces challenges, including limited data availability, algorithm validation, and seamless integration into clinical workflows. This paper reviews current literature on AI’s role in pathology, highlighting its capacity to improve diagnostic accuracy and efficiency while examining barriers to broader adoption. As technology advances, AI holds substantial promise to revolutionize the discipline, ultimately enhancing patient care and reducing diagnostic errors. By addressing challenges, AI-driven pathology may soon become a cornerstone of modern healthcare.