Abstract
Sebastian Weber, Marc Wyszynski, Marie Godefroid, Ralf Plattfaut, Bjoern Niehaves
Abstract
Authors
Institutions
No ROR-resolved institution is linked to this work yet.
Provenance
crossref
Confidence 100%
pubmed
Confidence 98%
europepmc
Confidence 96%
unpaywall
Confidence 95%
doaj
Confidence 92%
datacite
Confidence 0%
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Health care employees' perceptions of the use of artificial intelligence applications: survey study
10.2196/17620 · 2020
Are individual differences germane to the acceptance of new information technologies?
10.1111/j.1540-5915.1999.tb01614.x · 1999
MTurk research: review and recommendations
2021
10.1016/0749-5978(91)90020-t
10.1016/0749-5978(91)90020-t
2020 ACR data science institute artificial intelligence survey
2021
Accuracy of skin cancer diagnosis by physician assistants compared with dermatologists in a large health care system
10.1001/jamadermatol.2018.0212 · 2018
Unresolved referenced work
Kept as external metadata until matched
10.2196/15154
10.2196/15154
10.1609/icwsm.v14i1.7347
10.1609/icwsm.v14i1.7347
Artificial intelligence framework for simulating clinical decision-making: a markov decision process approach
10.1016/j.artmed.2012.12.003 · 2013
10.25300/misq/2021/16274
10.25300/misq/2021/16274
Physicians' resistance toward healthcare information technology: a theoretical model and empirical test
10.1057/palgrave.ejis.3000717 · 2007
Unresolved referenced work
Kept as external metadata until matched
Artificial intelligence and the future of primary care: exploratory qualitative study of UK general practitioners' views
10.2196/12802 · 2019
Computerization and the future of primary care: a survey of general practitioners in the UK
10.1371/journal.pone.0207418 · 2018
Latent dirichlet allocation
2003
Cognitive biases and heuristics in medical decision making: a critical review using a systematic search strategy
10.1177/0272989x14547740 · 2015
Bridging the "last mile" gap between ai implementation and operation: "data awareness" that matters
10.21037/atm.2020.03.63 · 2020
10.1111/j.1365-2923.2007.02803.x
10.1111/j.1365-2923.2007.02803.x
Perceptions of artificial intelligence among healthcare staff: a qualitative survey study
10.3389/frai.2020.578983 · 2020
Robotic services acceptance in smart environments with older adults: user satisfaction and acceptability study
10.2196/jmir.9460 · 2018
Unresolved referenced work
1988
Unresolved referenced work
Kept as external metadata until matched
The potential for artificial intelligence in healthcare
10.7861/futurehosp.6-2-94 · 2019
Technologies for an aging society: a systematic review of “smart home” applications
10.1055/s-0038-1638580 · 2008
Implications of psychometric measurement for neuropsychological interpretation
10.1080/13854046.2011.599819 · 2011
Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda
2019
Gender bias in medical research
10.1300/j015v12n04_06 · 1992
The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown?
10.1007/s00038-012-0416-3 · 2013
Unresolved referenced work
Kept as external metadata until matched
The importance of incorporating human factors in the design and implementation of artificial intelligence for skin cancer diagnosis in the real World
2020
Discovering and Categorising Language Biases in Reddit
10.1609/icwsm.v15i1.18048 · 2021
10.2196/16649
10.2196/16649
10.1038/s41746-021-00385-9
10.1038/s41746-021-00385-9
10.31234/osf.io/94xcp
10.31234/osf.io/94xcp
10.1007/978-3-031-41620-0_19
10.1007/978-3-031-41620-0_19
How to measure the status quo bias? A review of current literature
10.1007/s11301-022-00283-8 · 2023
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
Kept as external metadata until matched
An International Survey on AI in Radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude
10.1007/s00330-021-07781-5 · 2021
Radiation oncologists' perceptions of adopting an artificial intelligence-assisted contouring technology: model development and questionnaire study
10.2196/27122 · doi-reference
10.1038/s41551-018-0305-z
10.1038/s41551-018-0305-z · doi-reference
Role of artificial intelligence applications in real-life clinical practice: systematic review
10.2196/25759 · doi-reference
Individual differences moderate effects in an Unusual Disease paradigm: a psychophysical data collection lab approach and an online experiment
10.3389/fpsyg.2023.1086699 · doi-reference
Factors influencing the acceptability, acceptance, and adoption of conversational agents in health care: integrative review
10.2196/46548 · doi-reference
How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals
10.1038/s41591-021-01312-x · doi-reference
Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior
10.1080/10494820.2019.1674887 · doi-reference
A wikipedia-based method to support creative idea generation: the role of stimulus relatedness
10.1080/07421222.2019.1661095 · doi-reference
Ai in health: state of the art, challenges, and future directions
10.1055/s-0039-1677908 · doi-reference
A model of the antecedents of perceived ease of use: development and test
10.1111/j.1540-5915.1996.tb01822.x · doi-reference
10.1126/science.185.4157.1124
10.1126/science.185.4157.1124 · doi-reference
10.1038/s41591-020-0942-0
10.1038/s41591-020-0942-0 · doi-reference
A behavioral model of rational choice
10.2307/1884852 · doi-reference
Artificial intelligence-based conversational agents for chronic conditions: systematic literature review
10.2196/20701 · doi-reference
10.1007/bf00055564
10.1007/bf00055564 · doi-reference
Physician gender, patient risk, and web-based reviews: longitudinal study of the relationship between physicians' gender and their web-based reviews
10.2196/31659 · doi-reference
10.1145/2684822.2685324
10.1145/2684822.2685324 · doi-reference
Medical practitioner's adoption of intelligent clinical diagnostic decision support systems: A mixed-methods study
10.1016/j.im.2021.103524 · doi-reference
Scope and influence of electronic health record-integrated clinical decision support in the emergency department: a systematic review
10.1016/j.annemergmed.2018.10.034 · doi-reference
Intelligent therapeutic decision support for 30 days readmission of diabetic patients with different comorbidities
10.1016/j.jbi.2020.103486 · doi-reference
Physician confidence in artificial intelligence: an online mobile survey
10.2196/12422 · doi-reference
Strong artificial intelligence and consciousness
10.1142/s2705078520300042 · doi-reference
Investigating the acceptance of video consultation by patients in rural primary care: empirical comparison of preusers and actual users
10.2196/20813 · doi-reference
The assessment of alternative measures of consumer expertise
10.1086/209479 · doi-reference
Online information search behaviour of physicians
10.1111/hir.12170 · doi-reference
Reporting bias in medical research - a narrative review
10.1186/1745-6215-11-37 · doi-reference
10.1371/journal.pone.0274317
10.1371/journal.pone.0274317 · doi-reference
Emergency physician twitter use in the COVID-19 pandemic as a potential predictor of impending surge: retrospective observational study
10.2196/28615 · doi-reference
10.1080/19312458.2018.1430754
10.1080/19312458.2018.1430754 · doi-reference
Improving online idea generation platforms and customizing the task structure on the basis of consumers' domain-specific knowledge
10.1509/jm.13.0212 · doi-reference
Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France
10.1186/s12967-019-02204-y · doi-reference
Human factors and technological characteristics influencing the interaction of medical professionals with artificial intelligence-enabled clinical decision support systems: literature review
10.2196/28639 · doi-reference
10.2196/28151
10.2196/28151 · doi-reference
Investigating user resistance to information systems implementation: a status quo bias perspective
10.2307/20650309 · doi-reference
10.1257/jep.5.1.193
10.1257/jep.5.1.193 · doi-reference
Augmenting medical diagnosis decisions? An investigation into physicians’ decision-making process with artificial intelligence
10.1287/isre.2020.0980 · doi-reference
Identity threats as a reason for resistance to artificial intelligence: survey study with medical students and professionals
10.2196/28750 · doi-reference
Manipulating user resistance to large-scale information systems through influence tactics
10.1016/j.im.2019.103178 · doi-reference
Increasing acceptance of medical AI: the role of medical staff participation in AI development
10.1016/j.ijmedinf.2023.105073 · doi-reference