Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing

• URL: http://arxiv.org/abs/2607.27428v1
• Date: Wed, 29 Jul 2026 19:55:49 GMT
• Title: Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing
• Abstract summary: We argue that a decade of intense research has not translated into proportionate bedside impact.<n>This Perspective identifies six interconnected dimensions of this misalignment.<n>We propose a vision of agentic, physician-aligned AI that extends, rather than replaces, clinical judgment.
• Score: 29.25526289599026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 promise of foundation models in data-sparse medical domains; the persistent bottleneck of non-shareable, under-curated datasets; the gap between validated algorithms and deployable clinical platforms; and the failure of prediction-centric AI to generate actionable clinical guidance. For each dimension, we reframe the problem and propose a path forward, culminating in a vision of agentic, physician-aligned AI that extends, rather than replaces, clinical judgment.

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