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Chatbot Reliability in Managing Thoracic Surgical Clinical Scenarios.

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Four chatbots failed to accurately manage thoracic surgery scenarios, showing insufficient performance for reliable medical decision-making. This highlights concerns about current AI accuracy in complex surgical contexts.

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Area of Science:

  • Medical Artificial Intelligence
  • Thoracic Surgery
  • Clinical Decision Support

Background:

  • Growing integration of chatbots in medical practice necessitates evaluation of their accuracy.
  • Concerns exist regarding the reliability of AI tools in complex clinical scenarios.
  • This study focuses on chatbot performance within thoracic surgical contexts.

Purpose of the Study:

  • To assess the performance of four leading chatbots in managing thoracic surgical clinical scenarios.
  • To determine if current AI models meet acceptable accuracy thresholds for medical decision support.

Main Methods:

  • Developed 12 clinical scenarios across diagnosis, evaluation, and treatment domains.
  • Administered scenarios to ChatGPT-4, Gemini (Bard), Perplexity, and Claude 2 over three runs.
  • Scored scenarios based on Key Feature methods, with critical failures resulting in a score of 0.

Main Results:

  • All chatbots failed to achieve the passing threshold (≥2 points) across all runs, with critical mean scenario failure rates of 83%, 71%, and 71%.
  • While bots showed a trend towards learning between the first and second runs, no significant improvement in raw or adjusted scores was observed.
  • Overall performance indicated substantial variability and insufficiency in managing the presented clinical situations.

Conclusions:

  • Current chatbot performance in managing complex thoracic surgery scenarios is insufficient for reliable clinical assistance.
  • Findings serve as a cautionary note against premature reliance on AI for critical medical decision-making in specialized fields.
  • Further research is needed to improve AI accuracy and safety in healthcare applications.