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<p>Artificial intelligence (AI) is transforming scientific research by enabling the analysis of vast and complex data to address long-standing questions. Here, we offer our perspective on the applications of AI in palaeontology, focusing on how AI can change our understanding of biodiversity through deep time. We begin by exploring current methodologies used to automate fossil data processing, analyse morphological traits and model evolutionary patterns. Then, we discuss the limitations&#xa0;and challenges of data availability and quality and the extent to which AI-based models can capture biological and geological processes. Additionally, we highlight future avenues for exploiting AI modelling that have the potential to transform future research. Finally, we emphasize the importance of ensuring that the benefits of AI do not exacerbate existing inequalities in computing infrastructure. If AI can improve deep time biodiversity research, such improvement must be made available to the entire research community. Although AI has the potential to propel palaeontological research into a new era of discovery, it remains a relatively new tool, and its full capabilities have yet to be realized. Engaging with the challenges and opportunities presented by AI is essential to ensure inclusivity and progress in the palaeontological research community.</p>

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Emerging uses of artificial intelligence in deep time biodiversity research

  • 期刊论文

  • 发表日期：

    2025年8月11日

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  • Daniele Silvestro,
  • Catalina Pimiento

摘要

Artificial intelligence (AI) is transforming scientific research by enabling the analysis of vast and complex data to address long-standing questions. Here, we offer our perspective on the applications of AI in palaeontology, focusing on how AI can change our understanding of biodiversity through deep time. We begin by exploring current methodologies used to automate fossil data processing, analyse morphological traits and model evolutionary patterns. Then, we discuss the limitations and challenges of data availability and quality and the extent to which AI-based models can capture biological and geological processes. Additionally, we highlight future avenues for exploiting AI modelling that have the potential to transform future research. Finally, we emphasize the importance of ensuring that the benefits of AI do not exacerbate existing inequalities in computing infrastructure. If AI can improve deep time biodiversity research, such improvement must be made available to the entire research community. Although AI has the potential to propel palaeontological research into a new era of discovery, it remains a relatively new tool, and its full capabilities have yet to be realized. Engaging with the challenges and opportunities presented by AI is essential to ensure inclusivity and progress in the palaeontological research community.