Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2026 10 13 1727 1727 Arman Rahmim University of British Columbia Nourhan Bayasi Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, BC, Canada Xiaoxiao Li Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada Babak Saboury Institute of Nuclear Medicine, Bethesda, MD, USA Fereshteh Yousefirizi Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, BC, Canada 2026 09 27 2026 09 27 Medical imaging has served as primary proving ground for clinical AI, yet a decade of intense research has not translated into proportionate bedside impact. We argue that this gap is not primarily a product of insufficient algorithmic performance, inadequate regulation, or lim­ited explainability. Rather, it reflects a structural misalignment, between how AI systems are designed and evaluated, and how clinical decisions are made. This work identifies 8 interconnected dimensions of this misalignment: 1) the dominance of pixel-only models in a multimodal clinical world; 2) the erosion of physician trust through opaque and inflexible systems; 3) the unfulfilled promise of founda­tion models in data-sparse medical domains; 4) the persistent bottleneck of non-shareable under-curated datasets; 5) the gap between validated algorithms and deployable clinical platforms; 6) the slow, unmonitored translation of evidence into routine practice; 7) the risk of AI displacing rather than extending physician judgment; and 8) the failure of prediction-centric AI to generate actionable, agentic clinical guidance. For each dimension, we reframe the problem and propose a path forward, culminating in a vision of physician-centered AI solutions with agentic reasoning that extend, rather than replace, clinical judgment. https://fbt.tums.ac.ir/index.php/fbt/article/view/1727 https://fbt.tums.ac.ir/index.php/fbt/article/download/1727/593