Barriers to AI adoption and competitive strategy in medical imaging: a mixed-methods study

The study evaluates the accuracy of artificial intelligence (AI) in medical image diagnosis, analyzing obstacles such as rivalry in healthcare institutions. According to a quantitative analysis, AI outperformed radiologists in terms of sensitivity. Significant barriers to its adoption were identified through qualitative interviews, workflow disruptions, and significant disparities in adoption among hospitals. The results clearly demonstrate a dilemma between effectiveness and adoption, caused by a lack of institutional resources and a lack of trust in the technology. In order to bridge the gap between clinically fair integration and technological capabilities, the study proposes a complementary intelligence paradigm that supports interpretable AI systems, flexible regulatory standards, and equity-focused implementation strategies.