Background: Interactive artificial intelligence tools such as ChatGPT have gained popularity, yet little is known about their reliability as a reference tool for healthcare-related information for healthcare providers and trainees. The objective of this study was to assess the consistency, quality, and accuracy of the responses generated by ChatGPT on healthcare-related inquiries.
Methods: A total of 18 open-ended questions including six questions in three defined clinical areas (2 each to address "what", "why", and "how", respectively) were submitted to ChatGPT v3.5 based on real-world usage experience. The experiment was conducted in duplicate using 2 computers. Five investigators independently ranked each response using a 4-point scale to rate the quality of the bot's responses. The Delphi method was used to compare each investigator's score with the goal of reaching at least 80% consistency. The accuracy of the responses was checked using established professional references and resources. When the responses were in question, the bot was asked to provide reference material used for the investigators to determine the accuracy and quality. The investigators determined the consistency, accuracy, and quality by establishing a consensus.
Results: The speech pattern and length of the responses were consistent within the same user but different between users. Occasionally, ChatGPT provided 2 completely different responses to the same question. Overall, ChatGPT provided more accurate responses (8 out of 12) to the "what" questions with less reliable performance to the "why" and "how" questions. We identified errors in calculation, unit of measurement, and misuse of protocols by ChatGPT. Some of these errors could result in clinical decisions leading to harm. We also identified citations and references shown by ChatGPT that did not exist in the literature.
Conclusions: ChatGPT is not ready to take on the coaching role for either healthcare learners or healthcare professionals. The lack of consistency in the responses to the same question is problematic for both learners and decision-makers. The intrinsic assumptions made by the chatbot could lead to erroneous clinical decisions. The unreliability in providing valid references is a serious flaw in using ChatGPT to drive clinical decision making.
Keywords: Artificial intelligence; ChatGPT; Drug therapy; Healthcare decisions; Pharmacotherapy.
© 2024. The Author(s).