ReviewEndocrinology and metabolism (Seoul, Korea)2019
Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration.
Review in Endocrinology and metabolism (Seoul, Korea), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 65 citations in OpenAlex.
- AI in the Health Sector: Systematic Review of Key Skills for Future Health Professionals.JMIR medical education · 2025Pooled it
- Echo Chambers: Bias and Representation in Cardiac Imaging Datasets for Artificial Intelligence.Current cardiology reports · 2026Review
- Financial burden of glycemic control: a single-center retrospective study of diabetes costs in South Korean patients.The Korean journal of internal medicine · 2026Article
- Prevalence of Mortality and Vascular Complications in Older Patients with Diabetes in Korea.Endocrinology and metabolism (Seoul, Korea) · 2025Article
- Quantitative Evaluation of the Real-World Harmonization Status of Laboratory Test Items Using External Quality Assessment Data.Annals of laboratory medicine · 2024Article
- Effects of statin use on serum creatinine phosphokinase levels in normal thyroid function.The Korean journal of internal medicine · 2024Article
- Dark Data in Real-World Evidence: Challenges, Implications, and the Imperative of Data Literacy in Medical Research.Journal of Korean medical science · 2024Review
- Prediction of Cardiovascular Complication in Patients with Newly Diagnosed Type 2 Diabetes Using an XGBoost/GRU-ODE-Bayes-Based Machine-Learning Algorithm.Endocrinology and metabolism (Seoul, Korea) · 2024Article
- Comorbidity Patterns and Management in Inpatients with Endocrine Diseases by Age Groups in South Korea: Nationwide Data.Journal of personalized medicine · 2023Article
- Long-Term Cumulative Exposure to High γ-Glutamyl Transferase Levels and the Risk of Cardiovascular Disease: A Nationwide Population-Based Cohort Study.Endocrinology and metabolism (Seoul, Korea) · 2023Article
- A New Strategy for Evaluating the Quality of Laboratory Results for Big Data Research: Using External Quality Assessment Survey Data (2010-2020).Annals of laboratory medicine · 2023Article
- Review
- The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use.Journal of Korean medical science · 2023Review
- Multi-Omics and Management of Follicular Carcinoma of the Thyroid.Biomedicines · 2023Review
- Comparison of cardiocerebrovascular disease incidence between angiotensin converting enzyme inhibitor and angiotensin receptor blocker users in a real-world cohort.Journal of applied biomedicine · 2023Article
- Evolution of Hybrid Intelligence and Its Application in Evidence-Based Medicine: A Review.Medical science monitor : international medical journal of experimental and clinical research · 2023Review
- Long-Term Risk of Cardiovascular Disease Among Type 2 Diabetes Patients According to Average and Visit-to-Visit Variations of HbA1c Levels During the First 3 Years of Diabetes Diagnosis.Journal of Korean medical science · 2023Article
- KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness.Journal of personalized medicine · 2022Article
- Long-Term Changes in HbA1c According to Blood Glucose Control Status During the First 3 Months After Visiting a Tertiary University Hospital.Journal of Korean medical science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.