Najjar Reabal
Canberra Health Services, Australian Capital Territory 2605, Australia.
Diagnostics (Basel). 2023 Aug 25;13(17):2760. doi: 10.3390/diagnostics13172760.
This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology-data quality, the 'black box' enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. The conclusion underlines the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.
这篇全面综述详细讲述了人工智能（AI）涉足放射学的历程，这一举措正在推动医疗领域发生变革性转变。它追溯了放射学的发展历程，从最初发现X射线到机器学习和深度学习在现代医学图像分析中的应用。本综述的主要重点是阐明人工智能在放射学中的应用，阐释其在图像分割、计算机辅助诊断、预测分析和工作流程优化方面的重要作用。通过一系列跨多个医学学科的案例研究得出的实证证据，突出了人工智能对诊断过程、个性化医疗和临床工作流程的深远影响。然而，人工智能在放射学中的整合并非没有挑战。该综述深入探讨了人工智能驱动的放射学所固有的一系列障碍——数据质量问题、“黑匣子”谜团、基础设施和技术复杂性以及伦理影响。展望未来，该综述认为放射学中人工智能的前景充满希望。它主张持续开展研究，采用前沿成像技术，并促进放射科医生与人工智能开发者之间的紧密合作。结论强调了人工智能作为放射学变革催化剂的作用，这一立场深深植根于持续创新、动态合作以及对道德责任的坚定承诺。
