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

2026-07-29

AI Summary

This study addresses the structural misalignment between medical imaging AI research and clinical practice, which impedes the translation of algorithmic performance into tangible diagnostic and therapeutic value. The authors systematically identify six dimensions of misalignment across design, evaluation, and decision-making phases and propose a novel paradigm centered on clinicians, emphasizing actionability and multimodal integration. By incorporating multimodal modeling, interpretable interactive design, few-shot domain adaptation, and seamless integration into clinical workflows, the framework aims to develop “agentive, clinician-aligned” AI systems that generate actionable clinical guidance rather than mere predictions. This work offers the first structural analysis of implementation bottlenecks in medical AI and provides a systematic pathway to bridge the gap between artificial intelligence and real-world clinical practice, thereby enabling AI to genuinely support clinical judgment.

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.

Problem

• medical imaging
• artificial intelligence
• clinical decision-making
• structural misalignment
• AI deployment

Innovation

• physician-aligned AI
• structural misalignment
• agentic AI
• multimodal clinical integration
• actionable clinical guidance