Early adoption and perceived clinical impact of breast cancer detection artificial intelligence tools: Survey of society of breast imaging radiologists.

PURPOSE: To evaluate radiologists' early adoption, perceptions, and radiologist-reported clinical impact of breast cancer detection artificial intelligence (AI) tools in breast imaging practices. METHODS: An online survey was distributed to members of the Society of Breast Imaging to assess perceived clinical impact of FDA-cleared AI tools for mammographic breast cancer detection. Respondents were categorized as AI users or non-users. Responses were summarized descriptively, and differences were analyzed using Fisher's exact test. RESULTS: A total of 215 radiologists responded. Of these, 47% had implemented breast cancer detection AI tools (AI users), 11.2% are planning to implement them, and 41.8% had not implemented diagnostic AI tools (non-AI users). Among non-AI users, the most reported barrier to adoption was the implementation cost (53.3%). The most frequently perceived appropriate use of diagnostic AI tools was as a second reader. Non-AI users more often anticipated reductions in recall rates (59.3%) compared with AI users reporting reductions (34.7%; p = 0.003). A similar pattern was observed for biopsy rates (36.4% vs 9.1%; p < 0.001). Non-AI users also more frequently anticipated reduced burnout than AI users (56.0% vs 29.4%; p < 0.001). Perceptions of patient outcomes were similar between groups (p = 0.87). CONCLUSION: Although early adoption of breast cancer detection AI tools is notable, only a minority of AI users reported meaningful clinical benefit across measured outcomes, falling short of the benefits anticipated by non-AI users. Continued validation and refinement of AI tools is needed to ensure meaningful clinical impact.