THE MISSING LINK OF MACHINE LEARNING IN HEALTHCARE

The aim of this article is to show how the ambivalent nature of reality might impact artificial intelligence ( AI) use in medicine. The work illustrates that machine learning (ML) modelling requires some significant levels of data straight-jacketing to be efficient. However, data objectification will be counter-productive in the long run in AI-enabled medical contexts. The problem is that the ambivalent nature of realities requires a non-objectified modelling process, which is missing in machine learning at the moment. On the basis of this, the study hypothesizes that AI-enabled medicine will continue to depend largely on human intelligence to be efficient at least for the foreseeable future. The implication of this is that intelligent machines should be viewed as co-workers with man. The study draws from the theories of ontology in the Western continental tradition (especially the Heideggerian ontology) and the African philosophical tradition to ground the discourse.