  Data quality is the key factor for the development of trustworthy AI in
healthcare. A large volume of curated datasets with controlled confounding
factors can help improve the accuracy, robustness and privacy of downstream AI
algorithms. However, access to good quality datasets is limited by the
technical difficulty of data acquisition and large-scale sharing of healthcare
data is hindered by strict ethical restrictions. Data synthesis algorithms,
which generate data with a similar distribution as real clinical data, can
serve as a potential solution to address the scarcity of good quality data
during the development of trustworthy AI. However, state-of-the-art data
synthesis algorithms, especially deep learning algorithms, focus more on
imaging data while neglecting the synthesis of non-imaging healthcare data,
including clinical measurements, medical signals and waveforms, and electronic
healthcare records (EHRs). Thus, in this paper, we will review the synthesis
algorithms, particularly for non-imaging medical data, with the aim of
providing trustworthy AI in this domain. This tutorial-styled review paper will
provide comprehensive descriptions of non-imaging medical data synthesis on
aspects including algorithms, evaluations, limitations and future research
directions.
