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Letter to the Editor: Artificial intelligence in nephrology point-of-care ultrasonography - opportunities, limitations, and future directions
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
Revised: July 15, 2026
Accepted: July 27, 2026
Published online: September 25, 2026
Processing time: 55 Days and 9.2 Hours
Core Tip
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.