Barriers, Facilitators, and Intention to Use AI for Breast Cancer Diagnosis: Mixed Methods Study Among Austrian Physicians With and Without AI Experience.

backgroundArtificial intelligence (AI) has demonstrated strong potential in breast cancer diagnostics by improving accuracy, efficiency, and clinical workflow. However, adoption among physicians remains variable. Existing research often overlooks the contextual and experiential differences between clinicians who use AI and those who do not. A comprehensive understanding of barriers and facilitators, especially across user groups, is essential to inform equitable and effective AI implementation in real-world settings.

objectiveThis study aimed to (1) identify key barriers and facilitators influencing the use of AI tools in breast cancer diagnostics, with a specific focus on comparing current users and nonusers, and (2) examine how social, technological, and individual-level factors are linked to physicians' attitudes toward AI, intention to use it, and perceived likelihood of future adoption.

methodsA cross-sectional, embedded mixed methods survey was conducted with 46 Austrian physicians. Quantitative items were based on the technology acceptance model and its extensions. Open-ended responses were analyzed using conventional content analysis and integrated with quantitative results via joint displays. Ordinary least squares regressions examined factors associated with attitudes, intention, and the likelihood of future AI use.

resultsAmong the 46 participating physicians, 52% (n=24) reported current AI use. Common facilitators included improved quality of work, efficiency, and expanding knowledge. Nonusers highlighted barriers such as limited access (17/21, 81%), high costs, and lack of training. AI users highlighted barriers related to limited integration with existing systems and concerns about trust. Despite these differences, both groups expressed strong future adoption intentions. Perceiving multiple facilitators was significantly associated with more favorable attitudes (B=0.83; P=.02), stronger intention to use AI (B=1.32; P=.01), and higher perceived likelihood of future use (B=1.56; P=.001). AI-related skills positively predicted intention (B=1.00; P=.04) and likelihood of future use (B=1.16; P=.01), while colleagues' positive views about AI predicted both attitudes (B=0.34; P=.02) and intention (B=0.39; P=.01). In contrast, perceiving multiple barriers was associated with lower intention (B=-0.84; P=.047) and likelihood (B=-1.48; P<.001). Being aged 50 or older was significantly associated with more negative attitudes (B=-1.11; P=.002) and lower likelihood of future use (B=-0.82; P=.02).

conclusionsThis study offers preliminary insights into the implementation of AI in breast cancer diagnostics within the Austrian health care context. AI adoption appears to be a staged process with evolving support needs. Early-stage users may benefit from improved access and training, while experienced users require support for workflow integration and trust-building. Promoting peer support, addressing demographic disparities, and embedding AI training into clinical routines may support more sustainable and equitable adoption. These findings inform tailored implementation strategies and offer recommendations that may be transferable to other health systems.