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Acceptance of AI technology for lung cancer screening diagnosis: Unpacking the factors affecting the radiologists’ acceptance of AI

Harder, Jan Niklas LU and Massberg, Joachim Felix LU (2022) INFM10 20221
Department of Informatics
Abstract
Lung cancer is the leading cause of cancer death. Much benefit can be found in national screening programs to diagnose cancer early and reduce mortality
and such is about to be introduced in Germany. However, the process of screening diagnosis
is very time-intensive for radiologists and the workload is not feasible without further support.
Artificial Intelligence was assessed to be able to support radiologists and make lung cancer screening feasible. But many integrations of AI in healthcare fail due to lack of technology acceptance of physicians. Therefore, this research takes a qualitative approach to investigate
the technology acceptance of radiologists. Based on the Technology Acceptance Model and
the Task-Technology Fit... (More)
Lung cancer is the leading cause of cancer death. Much benefit can be found in national screening programs to diagnose cancer early and reduce mortality
and such is about to be introduced in Germany. However, the process of screening diagnosis
is very time-intensive for radiologists and the workload is not feasible without further support.
Artificial Intelligence was assessed to be able to support radiologists and make lung cancer screening feasible. But many integrations of AI in healthcare fail due to lack of technology acceptance of physicians. Therefore, this research takes a qualitative approach to investigate
the technology acceptance of radiologists. Based on the Technology Acceptance Model and
the Task-Technology Fit model a framework was developed to investigate the influential factors for radiologists’ acceptance of the AI. Five semi-structured interviews were conducted
with radiologists who work with the AI for lung cancer screening in currently ongoing Ger-
man trials and five factors were identified as influential: Perceived Usefulness, Perceived
Ease of Use/Training, Locatability, Relationship with Users, and Result Demonstrability. (Less)
Please use this url to cite or link to this publication:
author
Harder, Jan Niklas LU and Massberg, Joachim Felix LU
supervisor
organization
course
INFM10 20221
year
type
H1 - Master's Degree (One Year)
subject
keywords
User Perspective, Health Information Technology, Technology Acceptance Model, Task-Technology Fit, Lung Cancer Screening, Artificial Intelligence
report number
INF22-08
language
English
id
9081588
date added to LUP
2022-09-07 10:05:17
date last changed
2022-09-07 10:05:17
@misc{9081588,
  abstract     = {{Lung cancer is the leading cause of cancer death. Much benefit can be found in national screening programs to diagnose cancer early and reduce mortality 
and such is about to be introduced in Germany. However, the process of screening diagnosis 
is very time-intensive for radiologists and the workload is not feasible without further support. 
Artificial Intelligence was assessed to be able to support radiologists and make lung cancer screening feasible. But many integrations of AI in healthcare fail due to lack of technology acceptance of physicians. Therefore, this research takes a qualitative approach to investigate 
the technology acceptance of radiologists. Based on the Technology Acceptance Model and 
the Task-Technology Fit model a framework was developed to investigate the influential factors for radiologists’ acceptance of the AI. Five semi-structured interviews were conducted 
with radiologists who work with the AI for lung cancer screening in currently ongoing Ger-
man trials and five factors were identified as influential: Perceived Usefulness, Perceived 
Ease of Use/Training, Locatability, Relationship with Users, and Result Demonstrability.}},
  author       = {{Harder, Jan Niklas and Massberg, Joachim Felix}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Acceptance of AI technology for lung cancer screening diagnosis: Unpacking the factors affecting the radiologists’ acceptance of AI}},
  year         = {{2022}},
}