Barriers to the widespread adoption of diagnostic artificial intelligence for preventing antimicrobial resistance

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 chose..., 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 t..., , # 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 standardiz...,