  Artificial intelligence has provided us with an exploration of a whole new
research era. As more data and better computational power become available, the
approach is being implemented in various fields. The demand for it in health
informatics is also increasing, and we can expect to see the potential benefits
of its applications in healthcare. It can help clinicians diagnose disease,
identify drug effects for each patient, understand the relationship between
genotypes and phenotypes, explore new phenotypes or treatment recommendations,
and predict infectious disease outbreaks with high accuracy. In contrast to
traditional models, recent artificial intelligence approaches do not require
domain-specific data pre-processing, and it is expected that it will ultimately
change life in the future. Despite its notable advantages, there are some key
challenges on data (high dimensionality, heterogeneity, time dependency,
sparsity, irregularity, lack of label, bias) and model (reliability,
interpretability, feasibility, security, scalability) for practical use. This
article presents a comprehensive review of research applying artificial
intelligence in health informatics, focusing on the last seven years in the
fields of medical imaging, electronic health records, genomics, sensing, and
online communication health, as well as challenges and promising directions for
future research. We highlight ongoing popular approaches' research and identify
several challenges in building models.
