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Background <p>Dialysis nurses routinely make high-stakes clinical decisions under conditions of uncertainty, balancing protocol-based guidelines with contextual and experiential judgment. Recent advances in artificial intelligence (AI) raise questions regarding its potential role in supporting nursing clinical reasoning.</p> Aim <p>To compare clinical reasoning performance across experienced dialysis nurses, a general-purpose large language model (ChatGPT-4), and an agent-based AI system (MAI-DxO) using real-world nephrology scenarios, and to explore patterns of human nursing decision-making.</p> Design <p>A comparative, scenario-based study.</p> Methods <p>One hundred and ten dialysis nurses and two AI systems independently responded to four validated hemodialysis scenarios reflecting common clinical dilemmas. Responses were evaluated by senior nephrology nursing experts for diagnostic accuracy, appropriateness, and quality of clinical reasoning.</p> Results <p>The agent-based AI system achieved higher mean scenario scores than both ChatGPT-4 and nurses, particularly in structured justification and differential diagnosis. Nurses demonstrated greater variability, with strengths in contextual interpretation and recognition of dialysis-specific complications. Cluster analysis identified three distinct nursing reasoning profiles: protocol-driven, holistic-explanatory, and minimalist.</p> Conclusion <p>While AI systems can provide structured and guideline-consistent clinical reasoning, experienced dialysis nurses contribute contextual judgment and practical insight that remain essential to safe patient care. These findings support a complementary, rather than substitutive, role for AI in nursing clinical decision-making.</p>

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How do dialysis nurses and AI reason clinically? A scenario-based comparative study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • Brurya Orkaby,
  • Ronen Segev,
  • Mor Saban

摘要

Background

Dialysis nurses routinely make high-stakes clinical decisions under conditions of uncertainty, balancing protocol-based guidelines with contextual and experiential judgment. Recent advances in artificial intelligence (AI) raise questions regarding its potential role in supporting nursing clinical reasoning.

Aim

To compare clinical reasoning performance across experienced dialysis nurses, a general-purpose large language model (ChatGPT-4), and an agent-based AI system (MAI-DxO) using real-world nephrology scenarios, and to explore patterns of human nursing decision-making.

Design

A comparative, scenario-based study.

Methods

One hundred and ten dialysis nurses and two AI systems independently responded to four validated hemodialysis scenarios reflecting common clinical dilemmas. Responses were evaluated by senior nephrology nursing experts for diagnostic accuracy, appropriateness, and quality of clinical reasoning.

Results

The agent-based AI system achieved higher mean scenario scores than both ChatGPT-4 and nurses, particularly in structured justification and differential diagnosis. Nurses demonstrated greater variability, with strengths in contextual interpretation and recognition of dialysis-specific complications. Cluster analysis identified three distinct nursing reasoning profiles: protocol-driven, holistic-explanatory, and minimalist.

Conclusion

While AI systems can provide structured and guideline-consistent clinical reasoning, experienced dialysis nurses contribute contextual judgment and practical insight that remain essential to safe patient care. These findings support a complementary, rather than substitutive, role for AI in nursing clinical decision-making.