医疗影像人工智能（Artificial Intelligence, AI）作为医疗影像领域的重要技术，受到广泛关注与研究。医疗影像AI在疾病检测、诊断和治疗中发挥着关键作用，但目前在应用中仍面临众多挑战。本文对医疗影像AI的现状、主要方法和进展进行了系统性探讨，并对其在真实医疗场景中的表现进行了分析和总结。首先介绍了主要的医疗影像AI算法，包括AI映射模型、AI检测模型、AI分割模型和AI分类模型，并阐述了这些算法在医疗影像中的应用和进展。然后重点阐述了全栈全谱的理念，全面介绍了其在医疗影像中的应用，包括人工智能在MR成像、CT成像和PET成像中的影像重建应用与进展。接着描述了脑卒中一站式流程中的AI应用场景，包括出血性脑卒中和缺血性脑卒中的AI解决方案、危险因子的识别与管理，以及智能诊疗流程。进一步介绍了肺癌防治流程中的AI应用，从早期筛查和靶重建、表征量化分析、恶性风险评估，到三维术前规划、随访评估及结构化报告自动生成，全面展示了AI在肺癌防治中的应用。最后介绍了心血管AI全流程，包括冠状动脉精准成像、钙化积分智能早筛、三维分析辅助诊疗及其他疾病中的探索。本文总结了当前医疗影像AI的研究现状与未来发展方向，并对相关文献进行了回顾与分析，为后续研究提供了参考。

Medical imaging artificial intelligence (AI) is a crucial technology in the field of medical imaging, garnering significant attention. Medical imaging modalities widely used in clinical practice include magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), X-ray, and ultrasound, providing complementary information. AI technologies excel in mining image information and characterizing advanced features, driving constant innovation in core algorithms for applications such as disease detection, diagnosis, and treatment. This study systematically examines the current status, primary methods, and advancements of medical imaging AI, providing a thorough analysis and summary of its performance in real medical settings. The review begins by analyzing the key AI algorithms used in medical imaging, encompassing mapping models, detection models, segmentation models, and classification models, detailing their applications and progress in the field. Despite much of the research being applied sporadically within specific areas of medical imaging without substantial overall clinical workflow enhancements, this review emphasizes the concepts of full-stack and full-spectrum to introduce disruptive innovations and improvements to clinical workflows. “Full-stack” is dedicated to the development of medical imaging AI covering the entire process of pre-imaging, imaging, post-imaging, and functional assessment to improve imaging quality and diagnostic accuracy. In the pre-imaging phase, AI can intelligently handle positioning procedures, localization adjustments, and dose modulation. During imaging, AI reconstruction technology aids in generating fast and low-dose medical images. Post-imaging, AI-based quality control prevents image quality degradation while in functional evaluation, AI-based detection and segmentation help identify abnormalities. Additionally, AI-based classification supports disease diagnosis and treatment decisions, while AI-based registration technologies facilitate follow-up and disease progression monitoring. This review focuses on recent advancements in AI-based reconstruction for fast MRI, low-dose CT, and fast PET scenarios with the goal of improving image quality, accelerating scanning processes, reducing noise and artifacts while preserving the detailed structure of the lesion, and amplifying lesion contrast. Notably, functional assessment is critical for the full course of disease management by aiding at-risk identification, diagnosis, molecular subtyping, treatment planning, and prognostic evaluation. We anticipate that AI technologies can be integrated into the existing clinical workflow to enable full-stack analysis of a specific disease, improving patient outcomes and alleviating radiologists' workloads. “Full-spectrum” offers a different perspective by encompassing multiple imaging modalities supported by various imaging devices to accurately diagnose diseases independently or in combination using complementary modalities. It also broadens the application of AI to diverse diseases or body parts with the aim of diagnosing multiple conditions in a single scan for assessing structural and functional abnormalities throughout the body. Full-spectrum aims to improve healthcare by providing comprehensive diagnostic capabilities using advanced AI technology, to enable doctors to better understand a wide range of diseases and provide personalized medical diagnoses for different patient profiles. Drawing inspiration from the concepts of full-stack and full-spectrum, this review outlines several AI applications in real-world healthcare settings, focusing on one-stop diagnostics and management strategies for stroke, lung cancer, and cardiovascular diseases. Stroke management initiatives encompass solutions for both hemorrhagic and ischemic strokes, risk factor identification and management, as well as intelligent diagnostic protocols. The paper further explores AI approaches to lung cancer prevention and treatment, spanning early screening, target reconstruction, quantitative characteristic analysis, risk assessment, three-dimensional preoperative planning, follow-up evaluations, and structured report generation. Additionally, the review elaborates on the comprehensive cardiovascular AI process involving precise coronary artery imaging techniques, intelligent early screening for calcification scoring, three-dimensional analysis to aid diagnosis, and exploration into other cardiac conditions. It should be noted that a series of AI-based software has been developed to broaden the scope of AI interventions in the existing clinical workflow. In the context of the growing development of precision medicine, AI shows great potential in integrating multiple data streams into a powerful diagnostic or predictive system spanning radiomics, pathomics, and genomics, which is expected to accelerate the achievement of management goals that are truly tailored to the patient. The emergence and rapid development of generative AI technologies and large language models will lead to a series of innovative applications of generative AI, including scenarios such as interactive report interpretation, medical and health consulting, and smart operating rooms. The paper concludes by summarizing the current research status and outlining future development directions for medical imaging AI while providing a thorough review and analysis of pertinent literature to serve as a valuable reference for future research endeavors. We prospect the multidisciplinary cross-collaboration to promote high-quality clinical research, technological innovation, and software development, and expand new application scenarios for medical AI. By leveraging full-stack full-spectrum thinking and customized assembly of AI technology, its reach progressively extends across the full spectrum of clinical applications, imaging modalities, and disease types.