Do People Favor Artificial Intelligence Over Physicians? A Survey Among the General Population and Their View on Artificial Intelligence in Medicine.

OBJECTIVES: To investigate the general population's view on artificial intelligence (AI) in medicine with specific emphasis on 3 areas that have experienced major progress in AI research in the past few years, namely radiology, robotic surgery, and dermatology. METHODS: For this prospective study, the April 2020 Online Longitudinal Internet Studies for the Social Sciences Panel Wave was used. Of the 3117 Longitudinal Internet Studies For The Social Sciences panel members contacted, 2411 completed the full questionnaire (77.4% response rate), after combining data from earlier waves, the final sample size was 1909. A total of 3 scales focusing on trust in the implementation of AI in radiology, robotic surgery, and dermatology were used. Repeated-measures analysis of variance and multivariate analysis of variance was used for comparison. RESULTS: The overall means show that respondents have slightly more trust in AI in dermatology than in radiology and surgery. The means show that higher educated males, employed or student, of Western background, and those not admitted to a hospital in the past 12 months have more trust in AI. The trust in AI in radiology, robotic surgery, and dermatology is positively associated with belief in the efficiency of AI and these specific domains were negatively associated with distrust and accountability in AI in general. CONCLUSIONS: The general population is more distrustful of AI in medicine unlike the overall optimistic views posed in the media. The level of trust is dependent on what medical area is subject to scrutiny. Certain demographic characteristics and individuals with a generally positive view on AI and its efficiency are significantly associated with higher levels of trust in AI.

The integrated nuclear medicine and radiology residency program in the Netherlands: strengths and potential areas for improvement according to nuclear medicine physicians and radiologists.

PURPOSE: To evaluate the Dutch integrated nuclear medicine and radiology residency program from the perspective of nuclear medicine physicians and radiologists. METHODS: A survey was distributed among nuclear medicine physicians and radiologists in hospitals that participate in the Dutch integrated nuclear medicine and radiology training program. RESULTS: A total of 139 completed questionnaires were included. Nuclear medicine physicians (n = 36) assigned a mean score of 5.7 ± 2.0, and radiologists (n = 103) assigned a mean score of 6.5 ± 2.8 (on a 1-10 scale) to the success of the integrated training program in their hospital. On multiple regression, female gender of the survey participant (B = 2.22, P = 0.034), musculoskeletal radiology as subspecialty of the survey participant (B = 3.36, P = 0.032), and the survey participant's expectancy of resident's ability to handle workload after completion of residency were significantly associated with perceived success of the integrated training program (B = 1.16, P = 0.023). Perceived strengths of the integrated training program included broadening of expertise, a better preparation of future imaging specialists for hybrid imaging, increased efficiency in training residents, and increased efficiency in multidisciplinary meetings. Perceived weaknesses of the integrated training program included reduced exposure to nuclear medicine, less time for research and innovation, and concerns about its international recognition. CONCLUSION: This study provided insights into the experiences of nuclear medicine physicians and radiologists with the Dutch integrated nuclear medicine and radiology residency program, which may be helpful to improve the program and similar residency programs in other countries.

Artificial Intelligence in Screening Mammography: A Population Survey of Women's Preferences.

OBJECTIVE: To investigate the general population's view on the use of artificial intelligence (AI) for the diagnostic interpretation of screening mammograms. METHODS: Dutch women aged 16 to 75 years were surveyed using the Longitudinal Internet Studies for the Social sciences panel, representative for the Dutch population. Attitude toward AI in mammography screening was measured by means of five items: necessity of a human check; AI as a selector for second reading; AI as a second reader; developer is responsible for error; and radiologist is responsible for error. RESULTS: Of the 922 participants included, 77.8% agreed with the necessity of a human check, whereas the item AI as a selector for a second reading was more heterogeneously answered, with 41.7% disagreement, 31.5% agreement, and 26.9% responding with "neither agree nor disagree." The item AI as a second reader was mostly responded with "neither agree nor disagree" (37.1%) and "agree" (37.6%), whereas the two last items on developer's and radiologist' responsibilities were mostly answered with "neither agree nor disagree" (44.6% and 39.2%, respectively). DISCUSSION: Despite recent breakthroughs in the diagnostic performance of AI algorithms for the interpretation of screening mammograms, the general population currently does not support a fully independent use of such systems without involving a radiologist. The combination of a radiologist as a first reader and an AI system as a second reader in a breast cancer screening program finds most support at present. Accountability in case of AI-related diagnostic errors in screening mammography is still an unresolved conundrum.

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

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.