biomolecules Editorial Application of Artificial Intelligence for Medical Research Ryuji Hamamoto 1,2   Citation: Hamamoto, R. Application of Artificial Intelligence for Medical Research. Biomolecules 2021, 11, 90. https://doi.org/10.3390/biom11010090 Received: 7 January 2021 Accepted: 11 January 2021 Published: 12 January 2021 Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional clai- ms in published maps and institutio- nal affiliations. Copyright: © 2021 by the author. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; rhamamot@ncc.go.jp 2 Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan The Human Genome Project, completed in 2003 by an international consortium, is considered one of the most important achievements for mankind in the 21st century [1]. With the completion of this project, medical science has entered a new era known as the post-genome era [2]. In the latter half of the 20th century and into the 21st century, with the development of molecular biology, efforts to elucidate diseases at the molecular level were actively pursued worldwide. Under these circumstances, the completion of the whole human genome analysis has increased the momentum for personalized medicine, i.e., patient-specific medicine optimized by combining genome information and molecular medicine. Various technologies have been developed in the post-genome era; one of the major technological advances is the advent of next-generation sequencing (NGS). The duration of the human whole-genome analysis project undertaken by the international consortium was 13 years, costing USD 3 billion to analyze the entire genome of a single person [3]. However, with the advent of NGS, it now takes a day and less than USD 1000 for the same analysis [4]. Moreover, with the advent of such high-speed sequencers, the amount of data obtained in medical research is enormous, and the term “big data” is now common in medical research. In the 21st century, with the progress of machine learning technology (especially the emergence of deep learning technology) and graphics processing units, big data analysis using artificial intelligence (AI) technology is now common in various fields, including the medical field [5]. Its introduction in the medical field allows for a more objective analysis of biological phenomena, which are inherently complex and diverse, and require careful determination of the generalizability of the results obtained from analyses. In such an academic field, if scientific discussions involve only limited data, then it becomes difficult to grasp the complete picture of a phenomenon, and it is easy to fall into a state of “you can’t see the forest for the trees.” In contrast, the analysis of large-scale data using AI technology will make it possible to elucidate biological phenomena more objectively and without omission; it is expected to contribute greatly to the advancement of medicine. In fact, more than 60 AI-powered medical devices are approved by the Food and Drug Administration (FDA) in the United States, and the use of AI in the medical field is trending worldwide [2]. As shown in Figure 1, there are three areas in which AI technology is currently imple- mented in the medical field: medical image analysis, omics analysis, and natural language processing [2,5–9]. In this Special Issue on the “Application of Artificial Intelligence for Medical Research”, articles were presented in the fields of medical image analysis and omics analysis, among these three areas. As the editor of this Special Issue, I would like to provide a brief overview of it. Regarding radiation image analysis, Akatsuka et al. analyzed magnetic resonance images of the prostate with deep learning, compared them with observations by radiolo- gists and pathologists, and showed that deep learning could identify cancerous areas at a high rate, and could also find useful clues for clinical diagnosis even when the cancer was not visible [10]. Sukegawa et al. confirmed that panoramic radiographs could be Biomolecules 2021, 11, 90. https://doi.org/10.3390/biom11010090 https://www.mdpi.com/journal/biomolecules