A Qualitative Study to Understand Patient Perspective on the Use of Artificial Intelligence in Radiology

Haan, Marieke; Ongena, Yfke; Hommes, Saar; Kwee, Thomas C.; Yakar, Derya

doi:10.1016/j.jacr.2018.12.043

Cited by 76 publications

(105 citation statements)

References 7 publications

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“…The EFA generated five factors representing the following underlying latent variables: (1) Bdistrust and accountability of AI in radiology,^(2) Bprocedural knowledge of AI in radiology,^(3) Bpersonal interaction with AI in radiology,( 4) Befficiency of AI in radiology,^and (5) Bbeing informed of AI in radiology.F actors 1, 4, and 5 consist of a combination of items of the initial domains proof of technology, competence, and efficiency that were identified in our previous qualitative study [3]. Factors 2 (procedural knowledge) and 3 (personal interaction) correspond with the same domains as identified in the aforementioned qualitative study [3]. Originally, 17 items loaded on factor 1 Bdistrust and accountability.^Two items were dropped, to increase Cronbach's alpha to 0.863.…”

Section: Results Of Efamentioning

confidence: 99%

“…In this study, we documented the development of a standardized questionnaire to measure patients' attitudes towards AI in radiology. The questionnaire was developed on the basis of a previous qualitative study in a collaboration between radiologists and survey methodologists [3] and pretested for clarity and feasibility by means of cognitive interviews. Subsequently, 155 patients scheduled for CT, MRI, and/or conventional radiography on an outpatient basis filled out the questionnaire.…”

Section: Discussionmentioning

confidence: 99%

“…To develop the questionnaire, we conducted semi-structured qualitative interviews with 20 participants in a previous study (see Haan et al [3]). Based on these interviews [3], six key domains of patients' perspective on the implementation of AI in radiology were identified: proof of technology, procedural knowledge, competence, efficiency, personal interaction, and accountability. In the present study, we use these six domains as a framework for the questionnaire.…”

Section: Questionnaire Developmentmentioning

confidence: 99%

“…Importantly, unilateral development of AI systems from the perspective of the radiologist ignores the needs and expectations of patients who are perhaps the most important stakeholders. AI systems may need to fulfill certain preconditions for this technology to be embraced by society [3]. Patient preferences determine the boundaries within which an AI system should function.…”

Section: Introductionmentioning

confidence: 99%

“…Patient preferences determine the boundaries within which an AI system should function. At present, however, little is known on patients' views on the use of AI in radiology [3].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Results Of Efamentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Questionnaire Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Patient preferences determine the boundaries within which an AI system should function. At present, however, little is known on patients' views on the use of AI in radiology [3].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Perspectives of Youths on the Ethical Use of Artificial Intelligence in Health Care Research and Clinical Care

et al. 2023

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ImportanceUnderstanding the views and values of patients is of substantial importance to developing the ethical parameters of artificial intelligence (AI) use in medicine. Thus far, there is limited study on the views of children and youths. Their perspectives contribute meaningfully to the integration of AI in medicine.ObjectiveTo explore the moral attitudes and views of children and youths regarding research and clinical care involving health AI at the point of care.Design, Setting, and ParticipantsThis qualitative study recruited participants younger than 18 years during a 1-year period (October 2021 to March 2022) at a large urban pediatric hospital. A total of 44 individuals who were receiving or had previously received care at a hospital or rehabilitation clinic contacted the research team, but 15 were found to be ineligible. Of the 29 who consented to participate, 1 was lost to follow-up, resulting in 28 participants who completed the interview.ExposuresParticipants were interviewed using vignettes on 3 main themes: (1) health data research, (2) clinical AI trials, and (3) clinical use of AI.Main Outcomes and MeasuresThematic description of values surrounding health data research, interventional AI research, and clinical use of AI.ResultsThe 28 participants included 6 children (ages, 10-12 years) and 22 youths (ages, 13-17 years) (16 female, 10 male, and 3 trans/nonbinary/gender diverse). Mean (SD) age was 15 (2) years. Participants were highly engaged and quite knowledgeable about AI. They expressed a positive view of research intended to help others and had strong feelings about the uses of their health data for AI. Participants expressed appreciation for the vulnerability of potential participants in interventional AI trials and reinforced the importance of respect for their preferences regardless of their decisional capacity. A strong theme for the prospective use of clinical AI was the desire to maintain bedside interaction between the patient and their physician.Conclusions and RelevanceIn this study, children and youths reported generally positive views of AI, expressing strong interest and advocacy for their involvement in AI research and inclusion of their voices for shared decision-making with AI in clinical care. These findings suggest the need for more engagement of children and youths in health care AI research and integration.

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A Qualitative Study to Understand Patient Perspective on the Use of Artificial Intelligence in Radiology

Cited by 76 publications

References 7 publications

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

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

Perspectives of Youths on the Ethical Use of Artificial Intelligence in Health Care Research and Clinical Care

Stakeholder Engagement and Communication

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