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

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/Methods:This retrospective observational study evaluated agreement between AI-generated (ChatGPT) and nephrologistmade 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 Cramr'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% (Cramr'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.

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Journal
Medical Science Monitor
Published
2026-02-16
DOI
https://doi.org/10.12659/msm.951942
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
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article

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

Ali Veysel Kara, Serhat Hayme, Esin Öztürk, Ridvan Ozdemir et al.
Medical Science Monitor
Artificial Intelligence in Healthcare and Education
article

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

Ali Veysel Kara, Serhat Hayme, Esin Öztürk, Ridvan Ozdemir, Mahmut Sami Islamoglu
article en

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/Methods:This retrospective observational study evaluated agreement between AI-generated (ChatGPT) and nephrologistmade 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 Cramr'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% (Cramr'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.

Medical Science MonitorVol. 32
Erzincan Binali Yıldırım University (TR)
Peace, Justice and strong institutions
Openalex Percentile: Top 50%
Artificial Intelligence in Healthcare and Education
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