Koratala A, Ferreira F, Diniz H. Letter to the Editor: Artificial intelligence in nephrology point-of-care ultrasonography - opportunities, limitations, and future directions. World J Nephrol 2026; 15(3): 124607 [DOI: 10.5527/wjn.124607]
Corresponding Author of This Article
Abhilash Koratala, MD, Division of Nephrology and Surgical Critical Care, Medical College of Wisconsin, 8701 West Watertown Plank Road, Milwaukee, WI 53226, United States. akoratala@mcw.edu
Research Domain of This Article
Medicine, General & Internal
Article-Type of This Article
letter
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/
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Share the Article
Koratala A, Ferreira F, Diniz H. Letter to the Editor: Artificial intelligence in nephrology point-of-care ultrasonography - opportunities, limitations, and future directions. World J Nephrol 2026; 15(3): 124607 [DOI: 10.5527/wjn.124607]
World J Nephrol. Sep 25, 2026; 15(3): 124607 Published online Sep 25, 2026. doi: 10.5527/wjn.124607
Letter to the Editor: Artificial intelligence in nephrology point-of-care ultrasonography - opportunities, limitations, and future directions
Abhilash Koratala, Filipa Ferreira, Hugo Diniz
Abhilash Koratala, Division of Nephrology and Surgical Critical Care, Medical College of Wisconsin, Milwaukee, WI 53226, United States
Filipa Ferreira, Department of Nephrology, Unidade Local de Saúde de Braga, Braga 4200-319, Portugal
Hugo Diniz, Department of Nephrology, Centro Hospitalar E Universitário De São João, Porto 4200-319, Portugal
Author contributions: Koratala A conceived the idea for the manuscript, performed the literature review, drafted the manuscript; Ferreira F contributed to the conceptual development of the manuscript and critically reviewed the content; Diniz H supervised the work, provided conceptual guidance, and critically revised the manuscript; and all authors reviewed and approved the final version of the manuscript.
AI contribution statement: The scientific content, interpretations, and conclusions of this manuscript are entirely the work of the authors. Artificial intelligence tools were used only to assist with language editing and improving readability.
Conflict-of-interest statement: All authors declare that they have no conflict of interest to disclose.
Corresponding author: Abhilash Koratala, MD, Division of Nephrology and Surgical Critical Care, Medical College of Wisconsin, 8701 West Watertown Plank Road, Milwaukee, WI 53226, United States. akoratala@mcw.edu
Received: June 22, 2026 Revised: July 15, 2026 Accepted: July 27, 2026 Published online: September 25, 2026 Processing time: 55 Days and 9.2 Hours
Abstract
We read with interest the retrospective cohort study by Silipigni et al entitled “Role of point-of-care ultrasonography in kidney disease management: A single solution for multiple challenges”. Artificial intelligence (AI) is rapidly reshaping the way clinicians learn, perform, and interpret point-of-care ultrasonography (POCUS). In nephrology, AI-assisted image acquisition, automated measurements, and emerging decision-support tools offer opportunities to improve efficiency and expand access to POCUS training. At the same time, important limitations remain, particularly when ultrasound findings must be integrated with physiology and clinical context. In this commentary, we discuss the current role of AI in nephrology POCUS, examine potential pitfalls of overreliance on automated interpretation, and consider future applications that may extend beyond image analysis toward physiologic assessment and hemodynamic phenotyping. Ultimately, we suggest that AI should be used to enhance, not replace clinicians’ understanding of anatomy, physiology, and patient-specific pathophysiology.
Core Tip: Artificial intelligence (AI) has the potential to expand access to nephrology point-of-care ultrasonography through guidance of image acquisition, measurement automation, quality assurance, and clinical decision support. However, accurate image recognition does not necessarily equate to accurate physiologic interpretation, particularly in multi-organ hemodynamic assessment. AI should therefore be viewed as a tool that augments clinical judgment rather than replaces it.
