Yichen He , James M. Mulqueeney, Emily C. Watt, Arianna Salili-James, Nicole S. Barber, Marco Camaiti, Eloise S. E. Hunt, Oliver Kippax-Chui, Andrew Knapp, Agnese Lanzetti, Gizéh Rangel-de Lázaro, Jack K. McMinn, Joseph Minus, Ashwini V. Mohan, Lucy E. Roberts , Darcy Adhami, Enrico Grisan, Qianqian Gu, Victoria Herridge, Sanson T. S. Poon, Taylor West, Anjali Goswami

Artificial intelligence (AI) is poised to transform many aspects of society, and the study of evolutionary morphology is no exception. Classical AI methods such as Principal Component Analysis (PCA) and Cluster Analysis have been commonplace in evolutionary morphology for decades, but the last decade has seen increasing application of Deep Learning to ecology and evolutionary biology, opening up the potential to circumvent long standing barriers to rapid, big data analysis of phenotype. Here we review the current state of AI methods available for the study of evolutionary morphology and discuss the prospectus for near-term advances in specific subfields of this research area, including the potential of new AI methods that have not yet been applied to the study of morphological evolution. We introduce the main available AI techniques, categorising them into three stages based on their order of appearance: (i) Machine Learning, (ii) Deep Learning and (iii) the most recent advancements in large-scale models and multimodal learning. Next, we present existing AI approaches and case studies using AI for evolutionary morphology, including image capture and segmentation, feature recognition, morphometrics, phylogenetics, and biomechanics. Finally, we discuss areas where there is potential, but a lack of current applications of AI to key areas in evolutionary morphology. Combined, these advancements and potential developments have the capacity to transform the evolutionary analysis of the organismal phenotype into evolutionary phenomics, launch it fully in the “Big Data'' sphere, and align it with genomics and other areas of bioinformatics.