  Industrial Revolution 4.0 transforms healthcare systems. The first three
technological revolutions changed the relationship between human and machine
interaction due to the exponential growth of machine numbers. The fourth
revolution put humans into a situation where heterogeneous data is produced
with unmatched quantity and quality not only by traditional methods, enforced
by digitization, but also by ubiquitous computing, machine-to-machine
interactions and smart environment. The modern cyber-physical space underlines
the role of the person in the expanding context of computerization and big data
processing. In healthcare, where data collection and analysis particularly
depend on human efforts, the disruptive nature of these developments is
evident. Adaptation to this process requires deep scrutiny of the trends and
recognition of future medical data technologies` evolution. Significant
difficulties arise from discrepancies in requirements by healthcare,
administrative and technology stakeholders. Black box and grey box decisions
made in medical imaging and diagnostic Decision Support Software are often not
transparent enough for the professional, social and medico-legal requirements.
While Explainable AI proposes a partial solution for AI applications in
medicine, the approach has to be wider and multiplex. LLM potential and
limitations are also discussed. This paper lists the most significant issues in
these topics and describes possible solutions.
