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Translational Challenges of Biomedical Machine Learning Solutions in Clinical and Laboratory Settings

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Bioinformatics and Biomedical Engineering (IWBBIO 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13347))

Abstract

The ever increasing use of artificial intelligence (AI) methods in biomedical sciences calls for closer inter-disciplinary collaborations that transfer the domain knowledge from life scientists to computer science researchers and vice-versa. We highlight two general areas where the use of AI-based solutions designed for clinical and laboratory settings has proven problematic. These are used to demonstrate common sources of translational challenges that often stem from the differences in data interpretation between the clinical and research view, and the unmatched expectations and requirements on the result quality metrics. We outline how explicit interpretable inference reporting might be used as a guide to overcome such translational challenges. We conclude with several recommendations for safer translation of machine learning solutions into real-world settings.

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Notes

  1. 1.

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Authors and Affiliations

  1. Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg

    Carlos Vega, Miroslav Kratochvil, Venkata Satagopam & Reinhard Schneider

Authors
  1. Carlos Vega
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  2. Miroslav Kratochvil
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  3. Venkata Satagopam
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  4. Reinhard Schneider
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Corresponding author

Correspondence to Carlos Vega .

Editor information

Editors and Affiliations

  1. Marcelina Siebold Guest Relations Dept., University of Granada, Granada, Spain

    Ignacio Rojas

  2. Faculty of Sciences, University of Granada, Granada, Spain

    Olga Valenzuela

  3. ETSIIT. CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  4. ETSIIT, University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Vega, C., Kratochvil, M., Satagopam, V., Schneider, R. (2022). Translational Challenges of Biomedical Machine Learning Solutions in Clinical and Laboratory Settings. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13347. Springer, Cham. https://doi.org/10.1007/978-3-031-07802-6_30

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  • DOI: https://doi.org/10.1007/978-3-031-07802-6_30

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  • Print ISBN: 978-3-031-07801-9

  • Online ISBN: 978-3-031-07802-6

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