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Artificial intelligence chatbots like ChatGPT show potential for diagnostic support but are susceptible to biases present in patient history. While equivalent to residents in accuracy, ChatGPT

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • Diagnostic errors significantly impact patient care, often stemming from cognitive biases in clinical reasoning.
  • Artificial intelligence (AI) chatbots, such as ChatGPT, present a potential tool to mitigate these biases.
  • However, the susceptibility of AI chatbots to diagnostic biases remains largely unknown.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of ChatGPT compared to medical residents.
  • To assess ChatGPT's susceptibility to various types of cognitive biases in clinical reasoning.
  • To determine if AI chatbots can serve as reliable diagnostic support tools.

Main Methods:

  • The study compared the diagnostic performance of ChatGPT against 265 medical residents across five bias-inducing experiments.
  • Biases investigated included case-intrinsic factors (distracting findings, disruptive patient behaviors) and situational factors (prior availability of similar cases).
  • Diagnostic accuracy was measured by ChatGPT's ability to identify the most likely diagnosis.

Main Results:

  • ChatGPT's overall diagnostic accuracy was equivalent to that of medical residents.
  • Both ChatGPT and residents showed decreased accuracy when faced with case-intrinsic biases.
  • ChatGPT (4.0 and 3.5) demonstrated sensitivity to biases within patient history, while its performance was unaffected by situational biases, unlike residents.

Conclusions:

  • ChatGPT is sensitive to biases embedded in patient disease history but not to situational biases.
  • While AI chatbots have potential for diagnostic support, their use requires caution due to bias susceptibility.
  • Further research is needed to enhance AI's bias detection and mitigation capabilities for reliable clinical application.