journal article·2022-05-06Open access

Application of Artificial Intelligence in the Practice of Medicine

Hyo-Joong Suh, Junggab Son, Kyungtae Kang

Applied Sciences· Volume 12· Issue 9

Abstract

Advancements in artificial intelligence (AI) based on machine and deep learning are transforming certain medical disciplines [...]
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short

Research context

OpenAlex topics: Artificial Intelligence in Healthcare and Education · COVID-19 diagnosis using AI · Radiomics and Machine Learning in Medical Imaging

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Computer Science and Information Engineering, The Catholic University of Korea, Bucheon 14462, Korea

Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA

Department of Artificial Intelligence, Hanyang University, Ansan 15588, Korea

Funding

Catholic University of Korea, Research Fund, 2021 · Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (Ministry of Science and ICT) · Artificial Intelligence Convergence Research Center (Hanyang University ERICA) · High-Potential Individuals Global Training Program)

References (12)

Show source-provided references
1. Artificial Intelligence in Medicine: Current trends and future possibilities10.3399/bjgp18X695213 ↗
2. Artificial Intelligence in Medicine: Today and Tomorrow10.3389/fmed.2020.00027 ↗
3. Kim, H.J., Jo, E., Ryu, S., Nam, S., Song, S., Han, Y.S., Kang, T.S., Lee, W., Lee, S., and Kim, K.H. (2022). A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images. Appl. Sci., 12.10.3390/app12063190 ↗
4. Belenguer-Llorens, A., Sevilla-Salcedo, C., Desco, M., Soto-Montenegro, M.L., and Gómez-Verdejo, V. (2022). A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data. Appl. Sci., 12.10.3390/app12052571 ↗
5. Ju, M., Lee, M., Lee, J., Yang, J., Yoon, S., and Kim, Y. (2022). All You Need Is a Few Dots to Label CT Images for Organ Segmentation. Appl. Sci., 12.10.3390/app12031328 ↗
6. Hofmann, L.A., Lau, S., and Kirchebner, J. (2022). Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia. Appl. Sci., 12.10.3390/app12020819 ↗
7. Yoshida, M., Teramoto, A., Kudo, K., Matsumoto, S., Saito, K., and Fujita, H. (2022). Automated Extraction of Cerebral Infarction Region in Head MR Image Using Pseudo Cerebral Infarction Image by CycleGAN. Appl. Sci., 12.10.3390/app12010489 ↗
8. Gu, Y., Piao, Z., and Yoo, S.J. (2022). STHarDNet: Swin Transformer with HarDNet for MRI Segmentation. Appl. Sci., 12.10.3390/app12010468 ↗
9. Neuner, C., Coras, R., Blümcke, I., Popp, A., Schlaffer, S.M., Wirries, A., Buchfelder, M., and Jabari, S. (2022). A Whole-Slide Image Managing Library Based on Fastai for Deep Learning in the Context of Histopathology: Two Use-Cases Explained. Appl. Sci., 12.10.20944/preprints202110.0359.v2 ↗
10. Teramoto, A., Shibata, T., Yamada, H., Hirooka, Y., Saito, K., and Fujita, H. (2021). Automated Detection of Gastric Cancer by Retrospective Endoscopic Image Dataset Using U-Net R-CNN. Appl. Sci., 11.10.3390/app112311275 ↗
11. Matsuo, Y., and Takemoto, K. (2021). Backdoor Attacks to Deep Neural Network-Based System for COVID-19 Detection from Chest X-ray Images. Appl. Sci., 11.10.3390/app11209556 ↗
12. Galnares, M., Nesmachnow, S., and Simini, F. (2021). Instance-Based Learning Following Physician Reasoning for Assistance during Medical Consultation. Appl. Sci., 11.10.3390/app11135886 ↗