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- W4324133343 abstract "Abstract Importance Intelligent virtual assistants (IVAs) are ubiquitous and hold the potential to provide bystander cardiopulmonary resuscitation (CPR) instructions during an emergency. Objective To evaluate the quality of CPR instructions provided by IVAs. Design We evaluated the appropriateness of responses of four IVAs (Amazon Alexa, Apple Siri, Google Assistant, and Microsoft Cortana) to eight CPR-related questions. We also evaluated text-based responses provided by OpenAI ChatGPT, a recently developed artificial intelligence large language model. Results Out of 32 responses provided by IVAs, only 19 (59%) were related to CPR, 9 (28%) suggested calling emergency services, and 4 (12%) provided verbal CPR instructions. All responses provided by ChatGPT were related to CPR and suggested calling emergency services. Among responses related to CPR, the answers provided varied significantly in the utility of information provided. Conclusions and Relevance These results highlight the need for the technology industry to partner with the medical community to improve and standardize bystander CPR instruction provided by IVAs. Key points Question What is the quality of cardiopulmonary resuscitation (CPR) instruction provided by intelligent virtual assistants (IVAs) including Amazon Alexa, Apple Siri, Google Assistant, and Microsoft Cortana? Findings When asked questions related to CPR, IVAs frequently did not provide relevant information. Moreover, even when the information provided was relevant, actual CPR instruction was not given in most cases. Meaning These results highlight the need to standardize bystander CPR instruction provided by IVAs." @default.
- W4324133343 created "2023-03-15" @default.
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- W4324133343 date "2023-03-14" @default.
- W4324133343 modified "2023-09-29" @default.
- W4324133343 title "An Opportunity to Standardize and Enhance Intelligent Virtual Assistant-Delivered Layperson Cardiopulmonary Resuscitation Instructions" @default.
- W4324133343 cites W2063927032 @default.
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- W4324133343 doi "https://doi.org/10.1101/2023.03.09.23287050" @default.
- W4324133343 hasPublicationYear "2023" @default.
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