The impact of explanations on layperson trust in artificial intelligence-driven symptom checker apps: Experimental study

Journal article : Review

Abstract:: Background:
Artificial intelligence (AI)–driven symptom checkers are available to millions of users globally and are advocated as a tool to deliver health care more efficiently. To achieve the promoted benefits of a symptom checker, laypeople must trust and subsequently follow its instructions. In AI, explanations are seen as a tool to communicate the rationale behind black-box decisions to encourage trust and adoption. However, the effectiveness of the types of explanati... Expand abstract
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Files:: Woodcock_et_al_2021_Impact_of_Explanations.pdf

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Language:: English
Keywords:: conversational agent

clinical communication

explanations

FFR

mobile phone

diagnostics

trust

symptoms

symptom checker

chatbot

eHealth

virtual health care

artificial intelligence

knowledge

mHealth

digital health
Subtype:: Review
Pubs id:: 1213105
Local pid:: pubs:1213105
Deposit date:: 2022-05-13

Copyright holder:: Woodcock et al.
Rights statement:: ©2021 Claire Woodcock, Brent Mittelstadt, Dan Busbridge, Grant Blank. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.11.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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