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Artificial Intelligence Efficacy as a Function of Trainee Interpreter Proficiency: Lessons from a Randomized

David A Fussell1, Cynthia C Tang2, Jake Sternhagen2

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Artificial intelligence (AI) significantly improved diagnostic accuracy for medical students but not resident trainees in detecting intracranial hemorrhage and large-vessel occlusion. Medical students were also less likely to override incorrect AI suggestions.

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Area of Science:

  • Radiology
  • Medical Education
  • Artificial Intelligence in Medicine

Background:

  • Artificial intelligence (AI) tools are increasingly used in medical education and clinical practice.
  • The impact of AI on diagnostic accuracy across different trainee levels remains understudied.

Purpose of the Study:

  • To investigate how AI assistance affects the diagnostic accuracy of medical students and resident trainees.
  • To evaluate AI's impact on the detection of intracranial hemorrhage and large-vessel occlusion.

Main Methods:

  • Prospective study involving 48 medical students and resident trainees.
  • Participants interpreted 100 head CTs for intracranial hemorrhage and 100 head CTAs for large-vessel occlusion, with AI aid for one condition.
  • Outcomes included diagnostic accuracy (sensitivity, specificity) and interpretation time, analyzed using t-tests and ANOVA.

Main Results:

  • AI diagnostic aid significantly improved medical student accuracy by 11.0 points (P < .001); resident trainee accuracy showed no significant change.
  • Interpretation time for intracranial hemorrhage increased with AI, while large-vessel occlusion interpretation time decreased for medical students.
  • Both groups were less accurate when disagreeing with AI or when provided with incorrect AI results.

Conclusions:

  • AI demonstrated greater diagnostic accuracy improvement in medical students compared to resident trainees.
  • Medical students were less likely to override incorrect AI interpretations and remained less accurate than AI alone, even with assistance.