DOI: 10.5061/dryad.5mkkwh7f8
title: Data from: Barriers to the widespread adoption of diagnostic artificial intelligence for preventing antimicrobial resistance

description: Currently, antimicrobial resistance (AMR) poses a significant challenge
 and severely threatens public health, primarily due to the overuse of
 antimicrobials. In this survey, conducted across eight countries/areas, we
 assessed public preferences for two hypothetical types of artificial
 intelligence (AI): one prioritizing individual health (Individual
 precedence AI: abbr. Individual-AI) and the other considering the global
 threat of AMR (World precedence AI: abbr. World-AI). Our focus here is on
 the conflict (social dilemma) between recognizing the importance of AMR
 and desiring personalized treatment (for more details on the social
 dilemma, see Ito et al. 2022. Sci Rep. 12: 21084). The first question
 asked respondents about the desired adoption preference rates of World-AI
 versus Individual-AI. The second question inquired whether respondents
 were agree or disagree with the standardization of a single AI. The third
 question asked which type of AI, World or Individual -AI, should be chosen
 if standardization to a single AI was necessary. It is important to note
 that the only ideal response combination to resolve the issue of AMR would
 be a 100% adoption rate for World-AI (0% for Individual-AI), in favor of
 standardization, and choosing World-AI as the unified standard. This
 survey thus explores how the social dilemma inherent in AMR may impede the
 adoption of diagnostic AI.
description: The ‘Survey on Medical Advancement’ was conducted across 8
 countries/areas. The survey was conducted in four phases: the first phase
 was conducted in 2020/Jan/8~10 in Japan; the second phase was conducted in
 2020/Jul/1~7 in Japan, the US, and the UK; the third phase was conducted
 in 2021/May/18~26 in Sweden, Taiwan, and Australia; and the fourth phase
 was conducted in 2021/Jun/23~30 in Brazil and Russia.
 For the two surveys conducted in Japan, Cross Marketing, Inc.
 (https://www.cross-m.co.jp/en/), an internet survey company, created questionnaire webpages in accordance with our study design. Cross Marketing, Inc., also handled the data collection process. As of April 2020, Cross Marketing Inc. maintained an active panel (survey participants who registered in advance) of 4.79 million individuals, defined as survey participants who had been active within the last year. The questionnaire and response section were hosted on a website, allowing respondents to complete the survey and submit their responses. We obtained 500 submissions for each gender and age group by random sampling from all surveys collected during the survey periods. The surveys conducted in the other 7 countries/areas (i.e., the US, the UK, Sweden, Taiwan, Australia, Brazil, and Russia) were executed by Cint (https://www.cint.com/). Cint is the world’s largest consumer network for digital survey-based research and is headquartered in Sweden. Cint maintained a survey platform comprising more than 100 million consumer monitors in over 80 countries as of May 2020. For surveys conducted in the US, the UK, Sweden, Taiwan, Australia, Brazil, and Russia, Cint Japan (https://jp.cint.com/), the Japanese distributor of Cint, created translated questionnaire webpages following our study design. Cint Japan also managed the data collection process. We obtained at least 500 (the US, the UK, Sweden, Brazil, Russia) or 250 (Taiwan, Australia) submissions for each gender (male and female) and age group (20s, 30s, 40s, 50s, and 60s) by random sampling from all surveys collected during the survey periods. Note that both companies employed a sampling process to exclude inconsistent or apathetic respondents. For instance, individuals with discrepancies in their responses (e.g., those whose registered age did not match their reported age at the time of the survey) were excluded before the data reached the authors. Furthermore, respondents with notably short response times (i.e., less than 1 minute) were excluded from the analysis, as their quick responses may have indicated a lack of careful consideration of the survey questions. This study was approved by the Ethical Committee of the Institute of Tropical Medicine, Nagasaki University (Approval No. 190619217).
description: # Data from: Barriers to the widespread adoption of diagnostic artificial
 intelligence for preventing antimicrobial resistance
 [https://doi.org/10.5061/dryad.5mkkwh7f8](https://doi.org/10.5061/dryad.5mkkwh7f8) ## Description of the data and file structure The colum headers indicates as follows: \[A] no: ID number of respondents. \[B] Gender: Gender of respondents. '1' indicates 'male' '2' indicates 'female' \[C] Age: Age of respondents. In accordance with Dryad’s policy on human subjects data, individual ages have been masked by converting them into age ranges (e.g., "28" has been replaced with "20–29") to protect participant anonymity. \[D]&[E]: The adoption preference rate \[D] The adoption preference rate of World precedence AI '0-100%' \[E] The adoption preference rate of Individual precedence AI '0-100%' When the adoption preference rates of World precedence AI was X (%), the adoption rate of Individual precedence I was 100 – X (%). \[F]: Attitudes toward standardizing a single AI '1' indicates that the respondent select 'Agree' '2' indicates that the respondent select 'Disagree' \[G]: Which AI to standardize '1' indicates that the respondent select 'World precedence AI' '2' indicates that the respondent select 'Individual precedence AI' All the data related to this study are available at Data_S1.csv: Responses collected during the first survey in Japan (JPN1) Data_S2.csv: Responses collected during the second survey in Japan (JPN2) Data_S3.csv: Responses collected during the survey in the United States (US) Data_S4.csv: Responses collected during the survey in the United Kingdom (UK) Data_S5.csv: Responses collected during the survey in Sweden (SWE) Data_S6.csv: Responses collected during the survey in Taiwan (TWN) Data_S7.csv: Responses collected during the survey in Australia (AUS) Data_S8.csv: Responses collected during the survey in Brazil (BRA) Data_S9.csv: Responses collected during the survey in Russia (RUS).