AI has advanced tremendously over the last years and is expected to cause a new digital revolution in the coming decades . It is anticipated that radiology is one of the fields that will be transformed significantly. Many speculate about the potentially profound changes it will cause in the daily practice of a radiologist . However, there is a lack of debate on how patients would perceive such a transformation. For example, would patients trust a computer algorithm? Would they prefer human interaction over technology? To the best of our knowledge, there are no studies on this topic in the literature.
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  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.
An exploratory factor analysis, which took several rounds in the selection of factors and items within each factor, revealed five factors: (1) “distrust and accountability of AI in radiology,” (2) “procedural knowledge of AI in radiology,” (3) “personal interaction with AI in radiology,” (4) “efficiency of AI in radiology,” and (5) “being informed of AI in radiology.” Two of these factors (“procedural knowledge” and “personal interaction”) almost exactly corresponded with the domains identified in the qualitative study . For three factors (1, 2, and 3), the internal consistency was good (Cronbach’s alpha > 0.8); for one factor (4), it was acceptable (only just below 0.7); and for one factor (5), it was acceptable considering the lower number of items (n = 4) included (Cronbach’s alpha just below 0.6).
Some items of factor 5 loaded negatively, and although reverse coding easily solves this problem, it may also mean that items within this factor are multi-dimensional.
Factor 1 still included a large number of items. Since including many items will increase respondent burden, it may be worthwhile to reduce the number of items per scale, with preferably no more than 8 items per scale.
Thus, additional data collection with confirmatory factor analysis can be recommended to further refine the scale. Nevertheless, overall, the developed questionnaire provides a solid foundation to map patients’ views on AI in radiology.
Our findings with respect to associations between several demographic variables and trust and acceptance of AI are in line with earlier studies on acceptance of CHIT . As Or and Kash  concluded in their review of 52 studies examining 94 factors that predict the acceptance of CHIT, successful implementation is only possible when patients accept the technology and, to this end, social factors such as subjective norm (opinions of doctors, family, and friends) need to be addressed.
Interestingly, the results of our survey show that patients are generally not overly optimistic about AI systems taking over diagnostic interpretations that are currently performed by radiologists. Patients indicated a general need to be well and completely informed on all aspects of the diagnostic process, both when it comes to how and which of their imaging data are acquired and processed. A strong need of patients to keep human interaction also emerged, particularly when communicating the results of their imaging examinations. These findings indicate that it is important to actively involve patients when developing AI systems for diagnostic, treatment planning, or prognostic purposes, and that patient information and education may be valuable when AI systems with proven value are to enter clinical practice. They also signify the patients’ need for the development of ethical and legal frameworks within which AI systems are allowed to operate. Furthermore, the clear need for human interaction and communication also indicates a potential role for radiologists in directly counseling patients about the results of their imaging examinations. Such a shift in practice may particularly be considered when AI takes over more and more tasks that are currently performed by radiologists. Importantly, the findings of our survey only provide a current understanding on patients’ views on AI in general radiology.
The developed questionnaire can be used in future time points and in more specific patient groups that undergo specific types of imaging, which will provide valuable information on how to adapt radiological AI systems and their use to the needs of patients.
Limitations of our study include the fact that validation was done by means of cognitive interviews and exploratory factor analysis, which may be viewed as subjective. Validation with other criteria, such as comparison with existing scales, was not possible due to unavailability of such scales. Furthermore, our questionnaire was tested in patients on an outpatient basis, which may not be representative of the entire population of radiology patients.
In addition, although we explored the acceptability of purely AI-generated reports with patients, the acceptability of radiologist-written, AI-enhanced reports, which may well be the norm in the future, was not addressed.
It should also be mentioned that we did not systematically record the number and reasons of patients who were not able or refused to participate. Nevertheless, in the far majority of patients who did not participate, this was due to a lack of time.
In conclusion, our study yielded a viable questionnaire to measure acceptance among patients of the implementation of AI in radiology. Additional data collection may provide further refinement of the scale.