想知道医学影像AI领域的科研方向出了问题在哪？这篇Perspective论文直接点出算法竞赛之外的概念缺失，给实验室和期刊提出了改进建议。
这篇Perspective论文区分了算法创新（在固定问题定义内改进计算实现和性能）与概念创新（重新定义问题、衡量标准、临床相关性）。作者指出当前激励结构、培训路径和发表规范 disproportionately 奖励算法创新，尤其在早期研究者中，而低估了概念贡献。通过医学影像AI的代表性案例，论文展示概念基础不足如何导致目标错位、泛化脆弱和有限现实影响。最后给出针对研究者、导师、审稿人和期刊的可操作建议，以更好地识别和支持概念创新。
Beyond Algorithms: Conceptual Innovation in Medical Imaging AI
Artificial intelligence has driven rapid progress in medical imaging research, producing increasingly sophisticated algorithms and steady improvements on benchmark tasks. However, this algorithm-centric trajectory has also revealed a growing imbalance: while computational methods advance rapidly, the conceptual foundations that define imaging tasks, evaluation metrics, and clinical meaning sometimes remain underexamined. In this Perspective, we distinguish algorithmic innovation, which focuses on improving computational implementations and performance within a fixed problem definition, from conceptual innovation, which reframes what problems are posed, how success is measured, and why an approach is clinically relevant. We argue that prevailing incentive structures, training pathways, and publication norms disproportionately reward algorithmic novelty, particularly for early-career researchers, while at times undervaluing conceptual contributions that are essential for scientific maturation and clinical translation. Through representative examples from medical imaging AI, we show how insufficient conceptual grounding can lead to misaligned objectives, fragile generalization, and limited real-world impact. We conclude with actionable recommendations for researchers, mentors, reviewers, and journals to better recognize, support, and integrate conceptual innovation alongside algorithmic advances.