Endocrinol Metab.  2019 Dec;34(4):349-354. 10.3803/EnM.2019.34.4.349.

Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration

Affiliations
  • 1Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea. 01cadiz@hanmail.net
  • 2Department of Endocrinology and Metabolism, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • 3Department of Psychiatry, College of Medicine, The Catholic University of Korea, Seoul, Korea.

Abstract

Most people are now familiar with the concepts of big data, deep learning, machine learning, and artificial intelligence (AI) and have a vague expectation that AI using medical big data can be used to improve the quality of medical care. However, the expectation that big data could change the field of medicine is inconsistent with the current reality. The clinical meaningfulness of the results of research using medical big data needs to be examined. Medical staff needs to be clear about the purpose of AI that utilizes medical big data and to focus on the quality of this data, rather than the quantity. Further, medical professionals should understand the necessary precautions for using medical big data, as well as its advantages. No doubt that someday, medical big data will play an essential role in healthcare; however, at present, it seems too early to actively use it in clinical practice. The field continues to work toward developing medical big data and making it appropriate for healthcare. Researchers should continue to engage in empirical research to ensure that appropriate processes are in place to empirically evaluate the results of its use in healthcare.

Keyword

Artificial intelligence; Big data; Data science; Medical informatics; Deep learning; Machine learning

MeSH Terms

Artificial Intelligence
Delivery of Health Care
Empirical Research
Humans
Learning
Machine Learning
Medical Informatics
Medical Staff
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