AI-Assisted Dialysis Decision-Making: Assessing Agreement Between ChatGPT and Nephrologist in Initial Dialysis Indication and Prescription in Emergency and ICU Settings

Clinical Snapshot

Key Findings

• P Value: P<0.001 for modality agreement

• Effect Size: 100% agreement for dialysis initiation, 92.9% for modality selection

• Primary Outcome: Agreement between AI and nephrologist decisions

• Nnt Or Sensitivity: Cramer's V=0.87 indicating strong association for modality selection

• Confidence Interval: Not reported

Clinical Application

Limited by need for validated AI systems and integration with existing clinical workflows Would require validation within Australian nephrology practice patterns, TGA approval for clinical decision support systems, and alignment with ANZDATA registry protocols Adult patients requiring first-time dialysis in emergency and ICU settings

Abstract

BACKGROUND Artificial intelligence (AI) is increasingly explored as a clinical decision-support tool in nephrology; however, its real-world applicability for dialysis decision-making in emergency and intensive care unit (ICU) settings remains insufficiently studied. Hemodialysis initiation and prescription are complex, time-sensitive, and dynamic processes that require expert clinical judgment. MATERIAL AND METHODS This retrospective observational study evaluated agreement between AI-generated (ChatGPT) and nephrologist-made dialysis decisions in emergency and ICU settings. Adult patients undergoing first-time dialysis were included. Agreement was assessed for dialysis initiation, modality selection, and key prescription parameters. To ensure clinical relevance, continuous prescription variables were categorized into predefined ranges. Agreement was quantified using Gwet's AC1 coefficient and Cramer's V statistic. RESULTS Eighty-four patients were included. AI demonstrated 100% agreement with nephrologists regarding dialysis initiation. Overall agreement for dialysis modality selection was 92.9% (Cramer's V=0.87, P<0.001). Agreement for core dialysis prescription parameters - including blood flow rate, dialysate sodium, potassium, and calcium concentrations - was high across modalities (all P<0.001). Lower agreement was observed for ultrafiltration-related parameters, particularly ultrafiltration duration, reflecting the individualized and dynamic nature of volume management during dialysis. CONCLUSIONS AI-assisted decision support demonstrated high agreement with nephrologist decisions for initial dialysis initiation, modality selection, and core prescription parameters in emergency and ICU settings. Discrepancies were primarily confined to ultrafiltration-related decisions, underscoring the necessity of ongoing bedside clinical judgment. These findings support the role of AI as a decision-support tool rather than a replacement for clinician-led dialysis management.

References

1. 1.Kara, A. V., Ozturk, E. V., Islamoglu, M. S., Ozdemir, R. V., & Hayme, S. V. (2026). AI-Assisted Dialysis Decision-Making: Assessing Agreement Between ChatGPT and Nephrologist in Initial Dialysis Indication and Prescription in Emergency and ICU Settings. Medical Science Monitor, Article 951942. https://doi.org/10.12659/MSM.951942