Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
Acute Medical Unit, Royal Infirmary of Edinburgh, Edinburgh, UK.
Br J Hosp Med (Lond). 2024 Jul 30;85(7):1-13. doi: 10.12968/hmed.2024.0312.
Artificial intelligence has the potential to transform medical imaging. The effective integration of artificial intelligence into clinical practice requires a robust understanding of its capabilities and limitations. This paper begins with an overview of key clinical use cases such as detection, classification, segmentation and radiomics. It highlights foundational concepts in machine learning such as learning types and strategies, as well as the training and evaluation process. We provide a broad theoretical framework for assessing the clinical effectiveness of medical imaging artificial intelligence, including appraising internal validity and generalisability of studies, and discuss barriers to clinical translation. Finally, we highlight future directions of travel within the field including multi-modal data integration, federated learning and explainability. By having an awareness of these issues, clinicians can make informed decisions about adopting artificial intelligence for medical imaging, improving patient care and clinical outcomes.
人工智能有可能改变医学影像。要将人工智能有效地整合到临床实践中，需要对其能力和局限性有一个深入的了解。本文首先概述了人工智能在检测、分类、分割和放射组学等关键临床应用案例中的应用。它强调了机器学习中的基础概念，如学习类型和策略，以及训练和评估过程。我们提供了一个广泛的理论框架，用于评估医学影像人工智能的临床有效性，包括评估研究的内部有效性和可推广性，并讨论了临床转化的障碍。最后，我们强调了该领域的未来发展方向，包括多模态数据集成、联邦学习和可解释性。通过了解这些问题，临床医生可以就采用人工智能进行医学影像诊断做出明智的决策，从而改善患者的护理和临床结果。
