Medical big data is not yet available: Why we need realism rather than exaggeration

Research output: Contribution to journalReview articlepeer-review

53 Scopus citations

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.

Original languageEnglish
Pages (from-to)349-354
Number of pages6
JournalEndocrinology and Metabolism
Volume34
Issue number4
DOIs
StatePublished - 1 Dec 2019

Bibliographical note

Publisher Copyright:
Copyright © 2019 Korean Endocrine Society.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

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

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