Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing
Medical imaging has served as primary proving ground for clinical artificial intelligence (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 Perspective identifies six interconnected dimensions of this misalignment: the dominance of pixel-only models in a multimodal clinical world; the erosion of physician trust through opaque and inflexible systems; the unfulfilled promi...
| Authors | Arman Rahmim, Nourhan Bayasi, Xiaoxiao Li et al. |
|---|---|
| Primary category | physics.med-ph |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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