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Evaluating Artificial Intelligence and Traditional Learning Tools for Chest X-Ray Interpretation: A Descriptive

Gurtek Singh Samra1, Vashisht Ramoutar1, Kelley Chen1

  • 1Department of Medicine, University of Leicester, Leicester, UK.

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Artificial intelligence (AI) tools like Chester show promise for improving chest X-ray (CXR) interpretation skills, complementing traditional resources. Combining AI efficiency with comprehensive databases like Radiopaedia may optimize medical education.

Keywords:
artificial intelligenceasynchronouschest X‐raymedical educationradiologytechnology enhanced learning

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Area of Science:

  • Medical Education
  • Radiology
  • Artificial Intelligence

Background:

  • Chest X-ray (CXR) interpretation is a critical medical skill that students find challenging.
  • Traditional resources (Radiopaedia) and AI tools (Chester) offer different learning approaches.

Purpose of the Study:

  • To evaluate the effectiveness of Radiopaedia and Chester AI as educational tools for CXR interpretation.
  • To explore medical students' perspectives on using AI in learning.

Main Methods:

  • A standardized teaching session on CXR interpretation fundamentals was conducted.
  • Students used Chester AI and Radiopaedia to complete a workbook of complex CXR cases.
  • Focus groups were held to gather student feedback.

Main Results:

  • Chester AI was found efficient for revision but had limitations with complex cases.
  • Radiopaedia was comprehensive but less efficient for the specific task.
  • Students found AI tools promising but noted areas for improvement.

Conclusions:

  • AI tools like Chester can supplement traditional learning materials effectively.
  • A blended approach combining AI efficiency with comprehensive resources may enhance CXR interpretation skills.