Artificial intelligence (AI) is currently regaining enormous interest due to the success of machine learning (ML), and in particular deep learning (DL). Image analysis, and thus radiomics, strongly benefits from this research. However, effectively and efficiently integrating diverse clinical, imaging, and molecular profile data is necessary to understand complex diseases, and to achieve accurate diagnosis in order to provide the best possible treatment. In addition to the need for sufficient computing resources, suitable algorithms, models, and data infrastructure, three important aspects are often neglected: (1) the need for multiple independent, sufficiently large and, above all, high-quality data sets; (2) the need for domain knowledge and ontologies; and (3) the requirement for multiple networks that provide relevant relationships among biological entities. While one will always get results out of high-dimensional data, all three aspects are essential to provide robust training and validation of ML models, to provide explainable hypotheses and results, and to achieve the necessary trust in AI and confidence for clinical applications.

中文翻译：

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为什么仅凭成像数据还不够：基于AI的成像，组学和临床数据的集成。

由于机器学习（ML）尤其是深度学习（DL）的成功，人工智能（AI）当前正重新引起人们的极大兴趣。图像分析以及放射线学从这项研究中受益匪浅。但是，有效，高效地整合各种临床，影像和分子谱数据对于理解复杂疾病并实现准确诊断以提供最佳治疗是必要的。除了需要足够的计算资源，合适的算法，模型和数据基础结构之外，还经常忽略三个重要方面：（1）需要多个独立的，足够大的，尤其是高质量的数据集；（2）对领域知识和本体的需求；（3）要求多个网络提供生物实体之间的相关关系。虽然人们总是会从高维数据中获得结果，但是这三个方面对于提供强大的ML模型训练和验证，提供可解释的假设和结果以及获得对AI的必要信任和临床应用的信心都是必不可少的。