Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-19T04:52:55.737Z Has data issue: false hasContentIssue false

7 - Automation in Medical Imaging: Who Gets What AI Sees? Insights from the Adopters’ Perspective

Published online by Cambridge University Press:  28 March 2024

Tina Sikka
Affiliation:
Newcastle University
Get access

Summary

Introduction

The impact of digital technologies on labour has become in the past decade a focus of science and technologies studies (STS). The potential effects that a group of integrated technologies – connectivity, Big Data, the increase in computing capacity, forms of machine learning – could have on the quantity of employment and on the quality of working practices in most sectors generate widespread fears and, at times, hopes for profound social change. The rationale used to estimate these effects, in the first stage of research, was primarily based on utilitarian techno-economic categories, that is, oriented to assess (a) the technical feasibility of automation and (b) the cost and, therefore, any competitive advantage that could derive from implementation. Less attention was paid to other important non-techno-economic factors – sociotechnical, cognitive-behavioural, historical and cultural – that constitute the core of the interdisciplinary approach of STS.

In this context, the application of digital technologies and artificial intelligence to medical imaging constitutes an ideal case for this dominant cognitive approach. In a sector that has long been deeply invested in the use of digital technologies, first in preclinical research and then in the therapeutic field, medical imaging has been considered an activity inevitably destined to be improved by new technologies, in terms of the quantity and quality of diagnostic activities, while clinicians and technicians were seen as predestined to be sacrificed on the altar of progress.

Is this accurate? The first experiences of Computer Aided Diagnosis systems from the 1990s onwards showed that the unfolding of these implications is much more complex and multifaceted than the reductionism of rational economic models can represent. The first experiences based on the use of machine-learning systems in work processes that seem ideal for their application have led to interesting but limited results and, above all, these have not become fully institutionalized in healthcare systems.

This chapter intends to exemplify some non-techno-economic factors that could be at the basis of these contradictory results, in order to articulate analytical tools that can be used to study the effects of new technologies on work in healthcare and in other sectors.

Type
Chapter
Information
Genetic Science and New Digital Technologies
Science and Technology Studies and Health Praxis
, pp. 160 - 179
Publisher: Bristol University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×