Artificial intelligence in paleontology

Congyu Yu*, Fangbo Qin, Akinobu Watanabe, Weiqi Yao, Ying Li, Zichuan Qin, Yuming Liu, Haibing Wang, Qigao Jiangzuo, Allison Hsiang, Chao Ma, Emily J Rayfield, Michael Benton, Xing Xu

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review


The accumulation of large datasets and increasing data availability have led to the emergence of data-driven paleontological studies, which reveal an unprecedented picture of evolutionary history. However, the fast-growing quantity and complication of data modalities make data processing laborious and inconsistent, while also lacking clear benchmarks to evaluate data collection and generation, and the performances of different methods on similar tasks. Recently, artificial intelligence (AI) has become widely practiced across scientific disciplines, but not so much to date in paleontology where traditionally manual workflows have been more usual. In this study, we review >70 paleontological AI studies since the 1980s, covering major tasks including micro- and macrofossil classification, image segmentation, and prediction. These studies feature a wide range of techniques such as Knowledge-Based Systems (KBS), neural networks, transfer learning, and many other machine learning methods to automate a variety of paleontological research workflows. Here, we discuss their methods, datasets, and performance and compare them with more conventional AI studies. We attribute the recent increase in paleontological AI studies most to the lowering of the entry bar in training and deployment of AI models rather than innovations in fossil data compilation and methods. We also present recently developed AI implementations such as diffusion model content generation and Large Language Models (LLMs) that may interface with paleontological research in the future. Even though AI has not yet been a significant part of the paleontologist's toolkit, successful implementation of AI is growing and shows promise for paradigm-transformative effects on paleontological research in the years to come.
Original languageEnglish
Article number104765
JournalEarth-Science Reviews
Publication statusPublished - 30 Apr 2009


  • Paleontology
  • Palaeontology
  • artificial intelligence
  • machine learning
  • deep learning
  • classification
  • segmentation
  • prediction


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