Abstract
Accelerated biodiversity loss due to anthropogenic factors has led some researchers to suggest Earth may be approaching a 6 th mass extinction event. While artificial intelligence (AI) cannot counteract this biodiversity loss directly, AI has seen an increased adoption as a powerful tool for marine biodiversity monitoring, particularly by enabling the rapid and efficient collection and processing of large volumes of highly accurate biodiversity data. Such monitoring can, in turn, inform conservation strategies and evidence-based marine policy. This review aims to increase the accessibility of AI-based imaging techniques for marine biodiversity research and to help researchers make informed decisions on how these tools can effectively be applied to marine biodiversity studies. This review is intentionally framed as an introductory primer rather than a novel conceptual synthesis, aiming to consolidate and clarify foundational AI imaging concepts for a marine biodiversity audience. We compare traditional survey methods with AI-assisted approaches to marine data collection, explain the fundamental principles behind AI imaging workflows, and discuss common limitations and sources of bias associated with their implementation. By establishing this baseline knowledge, we review current applications of AI imaging in marine biodiversity monitoring, and outline practical pathways for integrating these systems into marine research programs. Ultimately, we aim for readers to gain a deeper understanding not only of how complex AI systems support marine biodiversity monitoring, but how best to deploy them responsibly and effectively to address the growing data challenges facing marine science today.