AI Applications in Palaeontology: Enhancing Fossil Analysis and Interpretation

The proliferation of large datasets has facilitated data-driven approaches in paleontology, offering novel insights into evolutionary history. However, managing complex data poses challenges, including laborious processing and a lack of standardized evaluation metrics. Despite the widespread use of artificial intelligence (AI) in other scientific domains, its adoption in paleontology remains limited. This study reviews over 70 AI studies in paleontology since the 1980s, encompassing tasks such as micro- and macrofossil classification, image segmentation, and prediction. Various machine learning solutions, including Knowledge-Based Systems have been employed to automate paleontological workflows. The rise in AI adoption is attributed primarily to improved accessibility rather than advancements in fossil data or methodologies. Additionally, emerging AI implementations like diffusion models and the potential for Large Language Models is evident for future integration with paleontological research. Although AI has yet to become integral to paleontologists' toolkits, its successful implementation suggests transformative prospects for the field.