©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Jan 6, 2025; 13(1): 99744
Published online Jan 6, 2025. doi: 10.12998/wjcc.v13.i1.99744
Published online Jan 6, 2025. doi: 10.12998/wjcc.v13.i1.99744
Machine learning applications in healthcare clinical practice and research
Nikolaos-Achilleas Arkoudis, Research Unit of Radiology and Medical Imaging, School of Medicine, National and Kapodistrian University of Athens, Athens 11528, Greece
Nikolaos-Achilleas Arkoudis, 2nd Department of Radiology, “Attikon” General University Hospital, Medical School, National and Kapodistrian University of Athens, Chaidari 12462, Greece
Stavros P Papadakos, Department of Gastroenterology, Laiko General Hospital, National and Kapodistrian University of Athens, Athens 11527, Greece
Author contributions: Arkoudis NA and Papadakos SP assisted with conceptualization, visualisation, writing the original draft, reviewing and editing the manuscript and supervising its preparation; all of the authors read and approved the final version of the manuscript to be published.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
Corresponding author: Nikolaos-Achilleas Arkoudis, MD, MSc, PhD, Consultant Physician-Scientist, Researcher, Research Unit of Radiology and Medical Imaging, School of Medicine, National and Kapodistrian University of Athens, 19 Papadiamantopoulou, Athens 11528, Greece. nick.arkoudis@gmail.com
Received: August 6, 2024
Revised: September 25, 2024
Accepted: October 15, 2024
Published online: January 6, 2025
Processing time: 92 Days and 22.4 Hours
Revised: September 25, 2024
Accepted: October 15, 2024
Published online: January 6, 2025
Processing time: 92 Days and 22.4 Hours
Core Tip
Core Tip: Across numerous diverse industries, machine learning (ML) is revolutionizing healthcare as well. It has demonstrated the potential to aid in disease diagnosis, treatment planning, decision-making, and outcome prediction, as well as improve clinical trial design and their success rates, often surpassing traditional methods. We highlight a study, published in the World Journal of Clinical Cases, where ML techniques proved superior to traditional statistical methods in analyzing factors affecting the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease.