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AI in the Hot Seat: Head-to-Head Comparison of Large Language Models and Cardiologists in Emergency Scenarios.

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Summary

Large language models (LLMs) show varied performance in cardiac emergencies. ChatGPT matched early-career interventional cardiologists in simulated catheterization lab scenarios, suggesting potential as decision-support tools.

Keywords:
ChatGPTcatheterization laboratorylarge language modelsmyocardial infarction

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Area of Science:

  • Artificial Intelligence in Medicine
  • Cardiovascular Interventions
  • Medical Decision Support

Background:

  • Clinical utility of large language models (LLMs) in critical cardiac emergencies is unknown.
  • This study assesses LLM performance in complex catheterization laboratory (Cath lab) scenarios.

Purpose of the Study:

  • To evaluate and compare the performance of advanced LLMs against interventional cardiologists in managing simulated cardiac emergencies.
  • To identify which LLMs perform best in high-stakes Cath lab scenarios.

Main Methods:

  • A cross-sectional study involving twelve inferior myocardial infarction scenarios.
  • Seven LLMs and five early-career interventional cardiologists responded to simulated cases.
  • Responses were anonymized and evaluated by thirty experienced interventional cardiologists.

Main Results:

  • Physicians achieved an average score of 80.68.
  • ChatGPT achieved the highest LLM score (87.4), outperforming early-career physicians (p < 0.05).
  • Claude (80.8) and DeepSeek (78.7) showed no significant difference from physicians, while Gemini, LLAMA, Qwen, and Bing performed significantly worse.

Conclusions:

  • Significant variability exists in LLM performance for cardiac emergencies.
  • ChatGPT demonstrated comparable performance to early-career interventional cardiologists in simulated Cath lab settings.
  • LLMs show promise as supplementary decision-support tools in interventional cardiology.