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Augmenting Community Nursing Practice With Generative AI: A Formative Study of Diagnostic Synergies Using

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Experienced nurses outperform generative AI (GenAI) in diagnostic accuracy for simulated cases. While GenAI is fast, human clinical reasoning remains superior for complex patient scenarios, indicating further research is needed.

Keywords:
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Area of Science:

  • Nursing Practice
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • Generative AI (GenAI) systems are increasingly explored for healthcare applications.
  • Evaluating the diagnostic accuracy and clinical decision-making capabilities of GenAI against human experts is crucial.

Purpose of the Study:

  • To compare the diagnostic accuracy and clinical decision-making of experienced community nurses with state-of-the-art GenAI systems.
  • To assess performance in simulated patient case scenarios.

Main Methods:

  • 114 Israeli community nurses and 3 GenAI models (ChatGPT-4, Claude 3.0, Gemini 1.5) evaluated 4 simulated medical case studies.
  • Responses were analyzed for accuracy, speed, and comprehensiveness, with GenAI responses assessed under word limits and without.

Main Results:

  • Nurses achieved higher average scores than GenAI, particularly when GenAI responses were limited in length.
  • GenAI models provided faster but more verbose responses, often including unnecessary information.
  • Gemini 1.5 and Claude 3.0 (full versions) demonstrated the highest accuracy among the GenAI models.

Conclusions:

  • GenAI shows potential for supporting nursing tasks but does not yet match human clinicians' holistic clinical reasoning.
  • Experienced nurses demonstrated superior contextual knowledge and the ability to provide concise, practical responses.
  • Further research is required to determine GenAI's role in substituting or augmenting nursing expertise.