Intelligence Augmentation and capability co-elevation in healthcare enabled by reasoned transparency

Abstract

Artificial intelligence (AI) systems in healthcare can have a significant impact on the performance of the actors involved, encouraging, for example, increasingly early diagnoses, personalized treatments and more accurate data management and processing techniques. However, these results may depend on increasingly profitable human-machine interactions, Intelligence Augmentation, and potential in terms of capability co-elevation. Starting from this assumption, this study aims to understand which can be the Intelligence Augmentation and capability co- elevation driver in healthcare. This conceptual paper has been carried out with the conceptual goal of delineating, so, to address the research question, a deductive reasoning approach was applied and the methodological approach followed has been based on the description of the theoretical background, definition of evidence from an illustrative case, Livongo Health, addressed by analyzing secondary data extrapolated from the website contents, and development conclusions. From the illustration case, insights have been outlined to understand how to achieve the objectives of Intelligence Augmentation and capability co-elevation: it has been observed how the reasoned transparency in AI systems can be understood as an enabling factor.