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Random and Systematic Errors01:20

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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'ChatGPT can make mistakes' warnings fail: A randomized controlled trial.

Yavuz Selim Kıyak1, Özlem Coşkun1, Işıl İrem Budakoğlu1

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AI warnings do not change medical students' diagnostic decisions. Students already distrust AI, indicating simple disclaimers are insufficient for calibrated trust in AI-assisted learning.

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Cognitive Psychology

Background:

  • AI systems like ChatGPT are increasingly used in clinical decision-making, necessitating an understanding of their fallibility.
  • Warnings are common but their impact on medical students' diagnostic behavior is largely unknown.
  • The Judge-Advisor System (JAS) theory provides a framework to explore how warnings affect advice-taking by influencing perceived advisor credibility.

Purpose of the Study:

  • To investigate if warnings about AI fallibility alter medical students' diagnostic behavior.
  • To examine the effect of warnings on advice-taking and perceived credibility of AI advisors.
  • To refine advice-taking theory in the context of AI-supported medical education.

Main Methods:

  • A randomized controlled trial involving 186 fourth-year medical students evaluating ambiguous clinical vignettes.
  • Students were assigned to receive AI feedback with or without a warning about AI fallibility.
  • Advice-taking was measured by diagnostic changes after AI feedback, analyzed using change rates, weight-of-advice (WoA), and mixed-effects models.

Main Results:

  • The presence of a warning did not significantly influence students' diagnostic changes (15.3% vs. 15.9%).
  • The weight-of-advice (WoA) was significantly lower than previously reported in JAS meta-analyses, indicating students generally underweight AI advice.
  • A tendency was observed for students in the warning group to provide more explanations for disagreeing with AI feedback.

Conclusions:

  • AI disclaimers do not alter medical students' diagnostic advice-taking behavior, as perceived credibility is already low.
  • A credibility threshold exists, beyond which cautionary cues have minimal impact on AI advice utilization.
  • Simple warnings are likely insufficient to ensure appropriate trust calibration when using AI in medical learning and decision-making.