Deep Learning and COVID-19: Two Pathways to Scientific Evolution Article Swipe
Hao Kang
,
Hanyan Dong
,
Yi Ding
,
Ziyi Jin
,
Luoyi Fu
,
Jiaxin Ding
,
Xinbing Wang
,
Lei Zhou
,
Chenghu Zhou
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15168912
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15168912
COVID-19 and deep learning have each marked pivotal milestones in the evolution of modern science. Since the onset of the pandemic, researchers from diverse disciplines have converged to address urgent, real-world challenges, while deep learning has catalyzed methodological innovation across fields. These two phenomena exemplify distinct scientific paradigms: spread-out science, which propagates novel ideas and methods, and merge-in science, which synthesizes existing knowledge to solve complex problems. We introduce the concept of sci-entropy, defined as the difference between the semantic entropy of a paper’s citations and references. Positive sci-entropy reflects the diffusion of new ideas (spread-out), whereas negative values indicate knowledge consolidation (merge-in). Our analysis, spanning deep learning, COVID-19, and 19 additional disciplines, reveals that scientific progress is governed by the dynamic interplay between these two forces. Excessively high sci-entropy may fragment research, while overly low values can stifle innovation. Our findings suggest that the balance between innovation and synthesis is fundamental to the trajectory of scientific development, offering a new framework for understanding interdisciplinary research and knowledge integration.
Related Topics
Concepts
Coronavirus disease 2019 (COVID-19)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
2019-20 coronavirus outbreak
Virology
Medicine
Infectious disease (medical specialty)
Outbreak
Disease
Pathology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app15168912
- OA Status
- gold
- References
- 62
- Related Works
- 10
- OpenAlex ID
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Deep Learning and COVID-19: Two Pathways to Scientific EvolutionWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-08-13Full publication date if available
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Hao Kang, Hanyan Dong, Yi Ding, Ziyi Jin, Luoyi Fu, Jiaxin Ding, Xinbing Wang, Lei Zhou, Chenghu ZhouList of authors in order
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https://doi.org/10.3390/app15168912Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/app15168912Direct OA link when available
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Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 2019-20 coronavirus outbreak, Virology, Medicine, Infectious disease (medical specialty), Outbreak, Disease, PathologyTop concepts (fields/topics) attached by OpenAlex
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