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Study reveals widespread reuse of AI models in scientific research

A new study on arXiv investigates the reuse of pre-trained deep learning models (PTMs) within the scientific process, particularly in natural sciences. The research quantifies PTM utilization across 17,718 open-access papers, finding that "Biochemistry, Genetics and Molecular Biology" leads in PTM reuse. The study identifies "adaptation" as the most common reuse pattern and highlights the "testing" stage of the scientific process as most impacted by PTM integration. AI

IMPACT Demonstrates growing reliance on pre-trained models in scientific research, potentially lowering barriers to entry for complex analyses.

RANK_REASON The cluster contains an academic paper published on arXiv detailing empirical findings about AI model reuse in science. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nicholas M. Synovic, Karolina Ryzka, Alessandra V. Vellucci Solari, Kenny Lyons, James C. Davis, George K. Thiruvathukal ·

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

    arXiv:2603.13584v2 Announce Type: replace-cross Abstract: 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 gui…