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European Radiology

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Open Access 01-02-2020 | Artificial Intelligence | Radiological Education

Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire

Authors: Yfke P. Ongena, Marieke Haan, Derya Yakar, Thomas C. Kwee

Published in: European Radiology | Issue 2/2020

Abstract

Objectives

The patients’ view on the implementation of artificial intelligence (AI) in radiology is still mainly unexplored territory. The aim of this article is to develop and validate a standardized patient questionnaire on the implementation of AI in radiology.

Methods

Six domains derived from a previous qualitative study were used to develop a questionnaire, and cognitive interviews were used as pretest method. One hundred fifty-five patients scheduled for CT, MRI, and/or conventional radiography filled out the questionnaire. To find underlying latent variables, we used exploratory factor analysis with principal axis factoring and oblique promax rotation. Internal consistency of the factors was measured with Cronbach’s alpha and composite reliability.

Results

The exploratory factor analysis revealed five factors on AI in radiology: (1) distrust and accountability (overall, patients were moderately negative on this subject), (2) procedural knowledge (patients generally indicated the need for their active engagement), (3) personal interaction (overall, patients preferred personal interaction), (4) efficiency (overall, patients were ambiguous on this subject), and (5) being informed (overall, scores on these items were not outspoken within this factor). Internal consistency was good for three factors (1, 2, and 3), and acceptable for two (4 and 5).

Conclusions

This study yielded a viable questionnaire to measure acceptance among patients of the implementation of AI in radiology. Additional data collection with confirmatory factor analysis may provide further refinement of the scale.

Key Points

• Although AI systems are increasingly developed, not much is known about patients’ views on AI in radiology.

• Since it is important that newly developed questionnaires are adequately tested and validated, we did so for a questionnaire measuring patients’ views on AI in radiology, revealing five factors.

• Successful implementation of AI in radiology requires assessment of social factors such as subjective norms towards the technology.

Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL (2018) Artificial intelligence in radiology. Nat Rev Cancer 18:500–510CrossRef

Thrall JH, Li X, Li Q et al (2018) Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol 15(3 Pt B):504–508

Haan M, Ongena YP, Kwee TC, Yakar D, Hommes S (2019) A qualitative study to understand patient perspective on the use of artificial intelligence in radiology. J Am Coll Radiol. https://doi.org/10.1016/j.jacr.2018.12.043

Garg AX, Adhikari NK, McDonald H et al (2005) Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 10:1223–1238CrossRef

Featherall J, Lapin B, Chaitoff A, Havele SA, Thompson N, Katzan I (2018) Characterization of patient interest in provider-based consumer health information technology: survey study. J Med Internet Res 4:e128. https://doi.org/10.2196/jmir.7766 CrossRef

Ahmad BI, Ahlan AR (2015, 2015) Reliability and validity of a questionnaire to evaluate diabetic patients’ intention to adopt health information technology: a pilot study. J Theor Appl Inf Technol 72.2 Available via http://www.jatit.org/volumes/Vol72No2/1Vol72No2.pdf. Accessed 15 August 2019

Davis FD, Bagozzi RP, Warshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manag Sci 8:982–1002CrossRef

Venkatesh V, Davis F (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 2:186–204CrossRef

Callegaro M, Manfreda KL, Vehovar V (2015) Web survey methodology. Sage, New York

10.

Höhne JK, Lenzner T (2018) New insights on the cognitive processing of agree/disagree and item-specific questions. J Surv Stat Methodol 3:401–417CrossRef

11.

De Regge M, Beirão G, Den Ambtman A, De Pourcq K, Dias J, Kandampully J (2017) Health care technology adaption for elderly: does the family matter? Available via http://hdlhandlenet/1854/LU-8526313 Accessed 15 August 2019

12.

Dillman DA, Smyth JD, Christian LM (2014) Internet, phone, mail, and mixed-mode surveys: the tailored design method. Wiley, New York

13.

Willis G (2005) Cognitive interviewing. A tool for improving questionnaire design. Sage, Thousand OaksCrossRef

14.

Osborne JW, Costello AB, Kellow JT (2008) Best practices in exploratory factor analysis. Best practices in quantitative methods. Sage, Thousand OaksCrossRef

15.

Kaiser HF (1960) The application of electronic computers to factor analysis. Educ Psychol Meas 1:141–151CrossRef

16.

O’Connor B (2018) SPSS, SAS, MATLAB, and R programs for determining the number of components and factors using parallel analysis and Velicer’s MAP Test Available via https://peopleokubcca/brioconn/nfactors/nfactorshtml. Accessed 15 August 2019

17.

Cattell RB (1966) The scree-test for the number of factors. Multivar Behav Res 2:254–276

18.

Peterson RA, Kim Y (2013) On the relationship between coefficient alpha and composite reliability. J Appl Psychol 1:194–198CrossRef

19.

R Core Team (2019) R: a language and environment for statistical computing. Version 3.5.3. R Foundation for Statistical Computing, Vienna, Austria. Available via http://www.R-project.org/. Accessed 15 August 2019

20.

Cho E, Kim S (2015) Cronbach’s coefficient alpha: well known but poorly understood. Organ Res Methods 2:207–230CrossRef

21.

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRef

22.

Mazurowski MA (2019) Artificial intelligence may cause a significant disruption to the radiology workforce. J Am Coll Radiol 8:1077–1082CrossRef

23.

Or CK, Karsh B (2009) A systematic review of patient acceptance of consumer health information technology. J Am Med Inform Assoc 4:550–560

Title: Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire
Authors: Yfke P. Ongena
Marieke Haan
Derya Yakar
Thomas C. Kwee
Publication date: 01-02-2020
Publisher: Springer Berlin Heidelberg
Keyword: Artificial Intelligence
Published in: European Radiology / Issue 2/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-019-06486-0

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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