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Large Language Model (LLM)-Powered Chatbots Fail to Generate Guideline-Consistent Content on Resuscitation and May Provide Potentially Harmful Advice

Published online by Cambridge University Press:  06 November 2023

Alexei A. Birkun Open the ORCID record for Alexei A. Birkun [Opens in a new window]  and
Adhish Gautam
Alexei A. Birkun*
Affiliation:
Department of General Surgery, Anaesthesiology, Resuscitation and Emergency Medicine, Medical Academy named after S.I. Georgievsky of V.I. Vernadsky Crimean Federal University, Simferopol, 295051, Russian Federation
Adhish Gautam
Affiliation:
Regional Government Hospital, Una (H.P.), 174303, India
*
Correspondence: Alexei A. Birkun, MD, DMedSc Medical Academy named after S.I. Georgievsky of V.I. Vernadsky Crimean Federal University Lenin Blvd, 5/7, Simferopol, 295051, Russian Federation E-mail: birkunalexei@gmail.com
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Abstract

Introduction:

Innovative large language model (LLM)-powered chatbots, which are extremely popular nowadays, represent potential sources of information on resuscitation for the general public. For instance, the chatbot-generated advice could be used for purposes of community resuscitation education or for just-in-time informational support of untrained lay rescuers in a real-life emergency.

Study Objective:

This study focused on assessing performance of two prominent LLM-based chatbots, particularly in terms of quality of the chatbot-generated advice on how to give help to a non-breathing victim.

Methods:

In May 2023, the new Bing (Microsoft Corporation, USA) and Bard (Google LLC, USA) chatbots were inquired (n = 20 each): “What to do if someone is not breathing?” Content of the chatbots’ responses was evaluated for compliance with the 2021 Resuscitation Council United Kingdom guidelines using a pre-developed checklist.

Results:

Both chatbots provided context-dependent textual responses to the query. However, coverage of the guideline-consistent instructions on help to a non-breathing victim within the responses was poor: mean percentage of the responses completely satisfying the checklist criteria was 9.5% for Bing and 11.4% for Bard (P >.05). Essential elements of the bystander action, including early start and uninterrupted performance of chest compressions with adequate depth, rate, and chest recoil, as well as request for and use of an automated external defibrillator (AED), were missing as a rule. Moreover, 55.0% of Bard’s responses contained plausible sounding, but nonsensical guidance, called artificial hallucinations, that create risk for inadequate care and harm to a victim.

Conclusion:

The LLM-powered chatbots’ advice on help to a non-breathing victim omits essential details of resuscitation technique and occasionally contains deceptive, potentially harmful directives. Further research and regulatory measures are required to mitigate risks related to the chatbot-generated misinformation of public on resuscitation.

Keywords

artificial hallucinationartificial intelligencecardiac arrestcardiopulmonary resuscitationchatbotlarge language model

Information

Type
Original Research
Information
Prehospital and Disaster Medicine , Volume 38 , Issue 6 , December 2023 , pp. 757 - 763
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Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the World Association for Disaster and Emergency Medicine

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