Home / Palaeobotanical Tools / Artificial intelligence in paleontology

M.L. Borowiec et al. (2022): Deep learning as a tool for ecology and evolution. In PDF, Methods Ecol. Evol., 13: 1640–1660. Note also here.
"... In this review we synthesize 818 studies using deep learning in the context of ecology and evolution to give a discipline-wide perspective
[...] Operating within the machine learning paradigm, deep learning can be viewed as an alternative to mechanistic modelling. It has desirable properties of good performance and scaling with increasing complexity ..."

M.A.D. During et al. (2024): Automated segmentation of synchrotron-scanned fossils. In PDF, bioRxiv,
See here as well.
"... we present a free, browser-based segmentation tool that reduces computational overhead by splitting volumes into small chunks
[...] Beyond the online tool, all our code is open source, enabling contributions from the palaeontology community to further this emerging machine learning ecosystem ..."

J.M. Ede (2021): Deep learning in electron microscopy. Open access, Machine Learning: Science and Technology.

L. Ghervase and M. Dinu (2025): What's New with the Old Ones: Updates on Analytical Methods for Fossil Research. Free access, Chemosensors. https://doi.org/10.3390/chemosensors13090328.
"... The present study synthesizes the recent trends in fossil research, emphasizing the most common techniques found in the specialized literature over the past 20 years ..."

E.M. Knutsen and D.A. Konovalov (2024): Accelerating segmentation of fossil CT scans through Deep Learning. In PDF, Scientific Reports, 14.
See likewise here.
"... Recent developments in Deep Learning have opened the possibility for automated segmentation of large and highly detailed CT scan datasets of fossil material
[...] we present a method for automated Deep Learning segmentation to obtain high-fidelity 3D models of fossils digitally extracted from the surrounding rock, training the model with less than 1%-2% of the total CT dataset ..."

! Y. Liu et al. (2025): Artificial Intelligence in Paleobotany and Palynology. In PDF, Geological Journal See likewise here.
Note table 1: Development of artificial intelligence in palynology studies from the 1980s to 2025.
"... The integration of AI, encompassing expert systems, neural networks, support vector machines, and other machine learning algorithms, has significantly automated a variety of paleontological research workflows. The application of AI in paleobotany involves multiple aspects such as image classification, image segmentation and prediction ..."

! S. Lu et al. (2026): Advancing biological taxonomy in the AI era: deep learning applications, challenges, and future directions. In PDF, Science China Life Sciences, 69: 37-50. https://doi.org/10.1007/s11427-025-3074-8.
See likewise here.
"... Biological taxonomy faces an inflection point. In this review, we trace its progress through three technology-driven eras—morphology, molecular, and today’s emerging artificial intelligence (AI)-driven stage
[...] We highlight the recent breakthroughs in deep learning and foundation models and argue that fully integrated, causality-aware models could deliver a step-change in biological taxonomy ..."

P. Raia et al. (2025): From linear measurements in multivariate analysis to computational palaeontology. In PDF, Bollettino della Società Paleontologica Italiana, 64: 349-358.

! M. Yaqoob et al. (2025): Advancing paleontology: a survey on deep learning methodologies in fossil image analysis. In PDF, Artificial Intelligence Review, 58. See also here.
Note figure 3: The timeline presents the evolution from traditional manual identification to the incorporation of AI in paleontology.
"... we comprehensively review state-of-the-art deep learning based methodologies applied to fossil analysis, grouping the studies based on the fossil type and nature of the task
[...] Finally, we discuss novel techniques for fossil data augmentation and fossil image enhancements ..."

! C. Yu et al. (2024): Artificial intelligence in paleontology. Free access, Earth-Science Reviews, 252.
"... The accumulation of large datasets and increasing data availability have led to the emergence of data-driven paleontological studies
[...] In this study, we review >70 paleontological AI studies since the 1980s
[...] we discuss their methods, datasets, and performance and compare them with more conventional AI studies ..."