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

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.

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

Fig. 1. Example of the conceptual [23] and operational definitions of diabetes mellitus. HbA1c, hemoglobin A1c; OGTT, oral glucose tolerance test; ICD-10, International Classification of Diseases 10th Revision; OHA, oral hypoglycemic agents.

Fig. 2. Examples of real cases requiring data quality management. (A) Various written data. Even though the laboratory test result is “<3,” it is also written as “1.” The physicians' role is defining and classifying data, and staff who are most familiar with the data should do so. (B) Example of incorrectly entered data. AST, aspartate aminotransferase; GOT, glutamate oxaloacetate transaminase; ALT, aspartate aminotransferase; GPT, glutamate pyruvate transaminase; HBsAg, hepatitis B surface antigen; HBsAb, hepatitis B surface antibody; HDL, high-density lipoprotein; LDL, low-density lipoprotein.