mlsanityData to 14 Oct 2026

An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process

arXiv 2603.13584 ↗First posted 13 Mar 2026cs.SE cs.AIcited by 1

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Abstract (arXiv)

Deep learning has achieved recognition for its impact within natural sciences, yet the prohibitive financial and technical cost of training models from scratch inhibit adoption. Following software engineering community guidance, natural scientists are reusing pre-trained deep learning models (PTMs) to amortize these costs. While prior works recommend PTM reuse patterns, we present the first empirical study of PTM reuse patterns in the natural sciences, quantifying the utilization and impact of PTM reuse within the scientific process across 17,718 peer reviewed, open access papers. Our results show that "Biochemistry, Genetics and Molecular Biology" has outpaced other natural scientific fields in PTM reuse, "adaptation" reuse is the most prevalent PTM reuse pattern identified across all natural science fields, and the "testing" stage of the scientific process has been most impacted by PTM integration.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 119 references that are in the map. First posted on arXiv 13 Mar 2026.

Builds on

15 of 119 references.

References · 15

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3 more
  1. 2024Oil & Water? Diffusion of AI Within and Across Scientific Fieldscited by 6
  2. 2021Influence of agent's self-disclosure on human empathycited by 2
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Citing papers per year: 2026: 1.

1 paper.

  1. 2026A Large-Scale Measurement of AI Bill of Materials Completeness in Hugging Face Modelscited by 0