Evidence map›Paper›PMID 37358940›Full record

ArticleAI & society2023

Beyond the hype: 'acceptable futures' for AI and robotic technologies in healthcare.

Giulia De Togni, S Erikainen, S Chan, S Cunningham-Burley

Abstract read
In one paragraph

Article in AI & society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Giulia De TogniThe University of Edinburgh; College of Medicine and Veterinary Medicine; Molecular, Genetic and Population Health Sciences; Usher Institute; Centre for Biomedicine, Self and Society, 23 Buccleuch Place, Edinburgh, EH8 9LN United Kingdom.ORCID 0000-0002-3114-2373
S ErikainenUniversity of Aberdeen, School of Social Science; Department of Sociology, Aberdeen, AB24 3FX United Kingdom.
S ChanThe University of Edinburgh; College of Medicine and Veterinary Medicine; Molecular, Genetic and Population Health Sciences; Usher Institute; Centre for Biomedicine, Self and Society, 23 Buccleuch Place, Edinburgh, EH8 9LN United Kingdom.
S Cunningham-BurleyThe University of Edinburgh; College of Medicine and Veterinary Medicine; Molecular, Genetic and Population Health Sciences; Usher Institute; Centre for Biomedicine, Self and Society, 23 Buccleuch Place, Edinburgh, EH8 9LN United Kingdom.

Funding

Wellcome Trust
6 · The paper itself

Abstract

AI and robotic technologies attract much hype, including utopian and dystopian future visions of technologically driven provision in the health and care sectors. Based on 30 interviews with scientists, clinicians and other stakeholders in the UK, Europe, USA, Australia, and New Zealand, this paper interrogates how those engaged in developing and using AI and robotic applications in health and care characterize their future promise, potential and challenges. We explore the ways in which these professionals articulate and navigate a range of high and low expectations, and promissory and cautionary future visions, around AI and robotic technologies. We argue that, through these articulations and navigations, they construct their own perceptions of socially and ethically 'acceptable futures' framed by an 'ethics of expectations.' This imbues the envisioned futures with a normative character, articulated in relation to the present context. We build on existing work in the sociology of expectations, aiming to contribute towards better understanding of how technoscientific expectations are navigated and managed by professionals. This is particularly timely since the COVID-19 pandemic gave further momentum to these technologies.

Indexed as

AIExpectationsHealthcareHypeRoboticsTechnoscientific imaginaries

Identifiers

PMID37358940
PMCPMC10122446

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.