DeepHealth: Review and challenges of artificial intelligence in health informatics

TL;DR

This paper reviews recent advances and challenges in applying artificial intelligence to health informatics, covering medical imaging, genomics, and electronic health records, and discusses future research directions.

Contribution

It provides a comprehensive overview of AI applications in health informatics over the past seven years, highlighting key challenges and promising research avenues.

Findings

AI improves disease diagnosis and treatment personalization.

Challenges include data heterogeneity, high dimensionality, and model interpretability.

Future directions involve addressing data biases and enhancing model reliability.

Abstract

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.