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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Artificial intelligence vs. emergency physicians: who diagnoses better?

Ali İhsan Kilci1, Ramazan Azim Okyay2, Erhan Kaya2

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Large language models (LLMs) showed higher diagnostic accuracy than human experts in simulated emergency settings. LLMs performed comparably to experts in selecting initial diagnostic tests, suggesting AI as a potential decision-support tool.

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Area of Science:

  • Emergency Medicine
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Emergency departments face diagnostic challenges.
  • Large Language Models (LLMs) are emerging AI tools with potential clinical applications.
  • Evaluating LLM performance against human experts is crucial for adoption.

Purpose of the Study:

  • To compare diagnostic accuracy of LLMs versus an emergency medicine specialist.
  • To assess LLM capabilities in selecting initial diagnostic tests.
  • To evaluate LLMs in simulated emergency department scenarios.

Main Methods:

  • Created simulated emergency case presentations (history, exam findings).
  • Compared diagnostic accuracy and test selection of a human expert and three LLMs (ChatGPT-4, 4o, 3.5-mini).
  • Assessed accuracy based on predefined correct diagnoses and appropriate first-line tests.

Main Results:

  • LLMs achieved higher diagnostic accuracy (97-99%) than the human expert (92%).
  • LLMs performed comparably to the human expert in initial diagnostic test selection (80-89% vs. 88%).
  • Most diagnostic errors involved cardiovascular and gastrointestinal cases.

Conclusions:

  • LLMs demonstrate acceptable diagnostic accuracy, exceeding human expert performance.
  • LLMs show comparable performance to human experts in initial test selection.
  • AI models show promise as decision-support tools in emergency medicine, warranting further real-world validation.