Status and Direction of Healthcare Data in Korea for Artificial Intelligence

Abstract

Recently, artificial intelligence (AI) has been highlighted in various areas including healthcare [1–4]. AI can be categorized into symbolic AI such as expert systems and machine learning (ML), which includes deep learning. Technically, recently mentioned AI refers to ML or deep learning. Deep learning, which is inspired by biological neurons, is a subcategory of machine learning algorithms [5]. Machine learning (including deep learning) requires a large amount of training data to improve performance. Therefore, to implement a good healthcare AI system, we need a vast amount of healthcare data. Many people believe there is a large amount of data in hospitals based on the wide adaptation of electronic medical records (EMR). They mentioned that the adoption rate of EMR in the United States was dramatically increased to 97% after the introduction of the Health Information Technology for Economic and Clinical Health (HITECH) Act [6] and the adoption rate of EMR in Korea is more than 92%. Nearly all hospitals in Korea also use the computerized physician order entry (CPOE) system. However, the EMR adoption rate is only 58.1%, and the fully comprehensive EMR adoption rate has dropped to 11.6% [7]. This implies a lack of digitalized data for healthcare AI research in Korea. Even though there is a large amount of data, having only a large quantity of data based on big data concepts may fail to achieve an applicable healthcare AI system. We need well-curated and labeled data. For example, 54 US licensed ophthalmologists and ophthalmology senior residents have reviewed 128,175 retinal images to build a well-curated dataset [3]. Current digitalized medical records require more in-depth curation to be used for research. Moreover, to realize precision medicine with the aid of AI methods, we need many new healthcare data types including genome and wearable data. Corresponding Author: Soo-Yong Shin Department of Computer Science and Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Korea Tel: +82-31-201-2543 E-mail: sooyong.shin@khu.ac.kr