  The prevalence of artificial intelligence (AI) has envisioned an era of
healthcare democratisation that promises every stakeholder a new and better way
of life. However, the advancement of clinical AI research is significantly
hurdled by the dearth of data democratisation in healthcare. To truly
democratise data for AI studies, challenges are two-fold: 1. the sensitive
information in clinical data should be anonymised appropriately, and 2.
AI-oriented clinical knowledge should flow freely across organisations. This
paper considers a recent deep-learning advent, dataset condensation (DC), as a
stone that kills two birds in democratising healthcare data. The condensed data
after DC, which can be viewed as statistical metadata, abstracts original
clinical records and irreversibly conceals sensitive information at individual
levels; nevertheless, it still preserves adequate knowledge for learning deep
neural networks (DNNs). More favourably, the compressed volumes and the
accelerated model learnings of condensed data portray a more efficient clinical
knowledge sharing and flowing system, as necessitated by data democratisation.
We underline DC's prospects for democratising clinical data, specifically
electrical healthcare records (EHRs), for AI research through experimental
results and analysis across three healthcare datasets of varying data types.
