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How Domain Engineering Can Help to Raise Adoption Rates of Artificial Intelligence in Healthcare

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Information Integration and Web Intelligence (iiWAS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14416))

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Abstract

Regardless of the often-claimed success of artificial intelligence (AI) and machine learning (ML), AI-based Digital Decision Support Systems (DDSSs) still suffer from low adoption rates. Much algorithmic research is done, but examples of AI bringing tangible benefits to the healthcare industry are rare. We argue that one of the reasons for low adoption rates is missing domain understanding and/or the heterogeneity of domain understanding among the DDSS developers and domain experts. To overcome this, we are working towards a methodology to utilize the Domain Engineering approach to create a shared common understanding of key concepts and relationships within the healthcare domain in a structured, formalized way. In the realm of complex interdisciplinary DDSS development within healthcare, the Domain Engineering approach can serve as a valuable instrument for bridging the gap between IT professionals and domain experts. It facilitates establishing a shared comprehension of the domain and hopefully contributes significantly to increasing the value and adoption rates of DDSSs in the clinical process. In this paper, we are proposing our work-in-progress ideas and preliminary results.

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Notes

  1. 1.

    http://www.hl7.org/.

  2. 2.

    https://loinc.org/.

  3. 3.

    https://icd.who.int/browse10/.

  4. 4.

    https://www.snomed.org/.

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Acknowledgements

This work in the project ‘ICT programme’ was supported by the European Union through the European Social Fund.

Author information

Authors and Affiliations

  1. Department of Health Technologies, Tallinn University of Technology, Ehitajate tee 5, 12616, Tallinn, Estonia

    Markus Bertl, Toomas Klementi & Peeter Ross

  2. Department of Software Science, Tallinn University of Technology, Ehitajate tee 5, 12616, Tallinn, Estonia

    Gunnar Piho

  3. East Tallinn Central Hospital, Ravi 18, 10138, Tallinn, Estonia

    Peeter Ross

  4. Information Systems Group, Tallinn University of Technology, Akadeemia tee 15a, 12169, Tallinn, Estonia

    Dirk Draheim

Authors
  1. Markus Bertl
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  2. Toomas Klementi
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  3. Gunnar Piho
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  4. Peeter Ross
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  5. Dirk Draheim
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Corresponding author

Correspondence to Markus Bertl .

Editor information

Editors and Affiliations

  1. Monash University, Melbourne, VIC, Australia

    Pari Delir Haghighi

  2. La Trobe University, Melbourne, VIC, Australia

    Eric Pardede

  3. University of Auckland, Auckland, New Zealand

    Gillian Dobbie

  4. University of Auckland, Auckland, New Zealand

    Vithya Yogarajan

  5. Udayana University, Denpasar, Indonesia

    Ngurah Agus Sanjaya ER

  6. Johannes Kepler University Linz, Linz, Austria

    Gabriele Kotsis

  7. Johannes Kepler University Linz, Linz, Austria

    Ismail Khalil

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Bertl, M., Klementi, T., Piho, G., Ross, P., Draheim, D. (2023). How Domain Engineering Can Help to Raise Adoption Rates of Artificial Intelligence in Healthcare. In: Delir Haghighi, P., et al. Information Integration and Web Intelligence. iiWAS 2023. Lecture Notes in Computer Science, vol 14416. Springer, Cham. https://doi.org/10.1007/978-3-031-48316-5_1

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  • DOI: https://doi.org/10.1007/978-3-031-48316-5_1

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