Jamal A, Singh S, Qureshi F. Synthetic data as an investigative tool in hypertension and renal diseases research. World J Methodol 2025; 15(1): 98626 [DOI: 10.5662/wjm.v15.i1.98626]
Corresponding Author of This Article
Fawad Qureshi, MD, Researcher, Division of Nephrology and Hypertension, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, United States. qureshi.fawad@mayo.edu
Research Domain of This Article
Medicine, Research & Experimental
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Methodol. Mar 20, 2025; 15(1): 98626 Published online Mar 20, 2025. doi: 10.5662/wjm.v15.i1.98626
Synthetic data as an investigative tool in hypertension and renal diseases research
Aleena Jamal, Som Singh, Fawad Qureshi
Aleena Jamal, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, United States
Som Singh, School of Medicine, University of Missouri Kansas City, Kansas, MO 64106, United States
Fawad Qureshi, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, United States
Author contributions: Jamal A administrated the delegation of the report; Singh S and Jamal A wrote the initial draft; Qureshi F provided critical review and designed the overall concept and outline of the manuscript.
Conflict-of-interest statement: The authors report no conflicts of interest in the creation of this manuscript.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Fawad Qureshi, MD, Researcher, Division of Nephrology and Hypertension, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, United States. qureshi.fawad@mayo.edu
Received: July 1, 2024 Revised: August 15, 2024 Accepted: August 29, 2024 Published online: March 20, 2025 Processing time: 90 Days and 3.3 Hours
Abstract
There is a growing body of clinical research on the utility of synthetic data derivatives, an emerging research tool in medicine. In nephrology, clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy. This is especially important given the epidemiology of chronic kidney disease, renal oncology, and hypertension worldwide. However, there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.
Core Tip: The application of synthetic data may help accelerate research development as a tool coupled with traditional, established research datasets. However, this tool is still in its early stages in this clinical area. The current literature focuses on three major areas as of current including renal cell carcinoma, chronic kidney disease, and blood pressure and hypertension.
