Artificial Intelligence in Health Care: Current Applications and Issues
- Authors
- Park, Chan-Woo; Seo, Sung Wook; Kang, Noeul; Ko, BeomSeok; Choi, Byung Wook; Park, ChangMin; Chang, Dong Kyung; Kim, Hwiuoung; Kim, Hyunchul; Lee, Hyunna; Jang, Jinhee; Ye, Jong Chul; Jeon, Jong Hong; Seo, Joon Beom; Kim, Kwang Joon; Jung, Kyu-Hwan; Kim, Namkug; Paek, Seungwook; Shin, Soo-Yong; Yoo, Soyoung; Choi, Yoon Sup; Kim, Youngjun; Yoon, Hyung-Jin
- Issue Date
- 2020-11-02
- Publisher
- KOREAN ACAD MEDICAL SCIENCES
- Citation
- JOURNAL OF KOREAN MEDICAL SCIENCE, v.35, no.42
- Abstract
- In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low; moreover, there are various concerns regarding the safety and reliability of AI technology implementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.
- Keywords
- DIABETIC-RETINOPATHY; BIG DATA; MACHINE; VALIDATION; DIAGNOSIS; DIABETIC-RETINOPATHY; BIG DATA; MACHINE; VALIDATION; DIAGNOSIS; Artificial Intelligence; Machine Learning; Health Care; Application; Issue
- ISSN
- 1011-8934
- URI
- https://pubs.kist.re.kr/handle/201004/117857
- DOI
- 10.3346/jkms.2020.35.e379
- Appears in Collections:
- KIST Article > 2020
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