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Making Biomedical Sciences publications more accessible for machines

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Abstract

With the rapidly expanding catalogue of scientific publications, especially within the Biomedical Sciences field, it is becoming increasingly difficult for researchers to search for, read or even interpret emerging scientific findings. PubMed, just one of the current biomedical data repositories, comprises over 33 million citations for biomedical research, and over 2500 publications are added each day. To further strengthen the impact biomedical research, we suggest that there should be more synergy between publications and machines. By bringing machines into the realm of research and publication, we can greatly augment the assessment, investigation and cataloging of the biomedical literary corpus. The effective application of machine-based manuscript assessment and interpretation is now crucial, and potentially stands as the most effective way for researchers to comprehend and process the tsunami of biomedical data and literature. Many biomedical manuscripts are currently published online in poorly searchable document types, with figures and data presented in formats that are partially inaccessible to machine-based approaches. The structure and format of biomedical manuscripts should be adapted to facilitate machine-assisted interrogation of this important literary corpus. In this context, it is important to embrace the concept that biomedical scientists should also write manuscripts that can be read by machines. It is likely that an enhanced human–machine synergy in reading biomedical publications will greatly enhance biomedical data retrieval and reveal novel insights into complex datasets.

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Funding

This work is supported by Fonds Wetenschappelijk Onderzoek: 42/FA010100/32/6484, 1198020N.

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Authors and Affiliations

  1. Receptor Biology Lab, Department of Biomedical Sciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium

    Joris Van Meenen, Hanne Leysen, Deborah Walter & Stuart Maudsley

  2. Antwerp Research Group for Ocular Science, Department of Translational Neurosciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium

    Joris Van Meenen

  3. Weill Cornell Medical College, New York, NY, USA

    Hongyu Chen

  4. Anet Library Automation, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium

    Rudi Baccarne

  5. Faculty of Pharmaceutical, Veterinary and Biomedical Sciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium

    Bronwen Martin

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  1. Joris Van Meenen
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Van Meenen, J., Leysen, H., Chen, H. et al. Making Biomedical Sciences publications more accessible for machines. Med Health Care and Philos 25, 179–190 (2022). https://doi.org/10.1007/s11019-022-10069-0

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