Observational studyMedical science monitor : international medical journal of experimental and clinical research2026
AI-Assisted Dialysis Decision-Making: Assessing Agreement Between ChatGPT and Nephrologist in Initial Dialysis Indication and Prescription in Emergency and ICU Settings.
Observational study in Medical science monitor : international medical journal of experimental and clinical research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Evaluating the potential of ChatGPT as an educational decision-support tool for hemodialysis decision-making in nephrology training.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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