Core Viewpoint - The article emphasizes the importance of addressing data challenges in the medical imaging sector, which not only facilitates the revolutionary development of medical AI but also provides valuable experiences and models for AI applications across various industries [1]. Group 1: AI in Medical Imaging - The National Health Commission of China has set a timeline for the development of "AI + Healthcare," aiming for comprehensive coverage of intelligent diagnostic applications in primary care by 2030 [2]. - The AI medical imaging industry has matured, with major hospitals adopting AI products for diagnostic assistance [3]. - AI has significantly improved the efficiency of medical imaging diagnostics, reducing the time required for doctors to complete reports from approximately 30 minutes to 5-10 minutes, thus alleviating the workload of overburdened radiologists [5][6]. Group 2: Commercialization Challenges - Despite the substantial value created by AI in medical imaging, the industry faces a commercialization dilemma, with cumulative revenues projected to be less than 3 billion yuan from 2020 to 2024 [8]. - The low technical barriers and intense competition have led to a market where many companies offer similar products, often resorting to free trials to gain hospital access, which undermines profitability [9][10]. - Many hospitals, especially lower-tier ones, struggle with budget constraints, limiting their ability to invest in AI products, which further compresses the market potential [10]. Group 3: Future Potential of AI - To unlock greater potential, AI must enhance its value in medical imaging analysis, diagnosis, and treatment, which requires higher research and development barriers [12]. - Current AI models primarily based on Convolutional Neural Networks (CNN) have limitations in understanding complex medical images, while the introduction of Transformer models could significantly improve diagnostic capabilities [13][14]. - The integration of multi-modal data processing through Transformer models could lead to comprehensive clinical decision-making models, breaking down barriers between different types of medical data [14]. Group 4: Data Challenges - The transition from CNN to Transformer-based models presents significant data challenges, as training such models requires vast amounts of high-quality labeled data, which is difficult to obtain in the medical field due to privacy regulations [18][19]. - The complexity of multi-modal data integration further complicates the data landscape, necessitating extensive coordination and processing efforts [19]. - Addressing data issues is crucial for advancing AI in medical imaging, and companies that can establish robust capabilities in data collection, governance, and utilization will likely lead the next generation of medical AI [20].

医疗影像（X光片、CT、MRI、超声等）指利用各种成像技术，将人体内部的结构或组织以可视化的形式呈现出来，对疾病的诊断、治疗和监测起到重要的 作用。 刘劲、段磊、李嘉欣/文 近日，国家卫生健康委办公厅等五部门发布《关于促进和规范"人工智能+医疗卫生"应用发展的实施意见》，提出"人工智能+医疗卫生"发展的时间表：到 2030年，基层诊疗智能辅助应用基本实现全覆盖，推动实现二级以上医院普遍开展医学影像智能辅助诊断、临床诊疗智能辅助决策等人工智能技术应 用，"人工智能+医疗卫生"应用标准规范体系基本完善，建成一批全球领先的科技创新和人才培养基地。 当前，中国的医疗影像智能化建设确实正在提速，推广医学影像智能诊断服务，为提升基层医疗服务能力提供新路径。 由于医疗影像的数字化起步较早，数据结构相对标准化，便于计算机视觉处理，早在90年代，业界便开始尝试将医疗影像与计算机辅助诊断相结合；后来， 以卷积神经网络（CNN）为代表的深度学习技术在图像识别领域取得巨大突破。自2017年左右起，AI技术与医疗影像的研究、临床试验和实际应用开始快 速发展，成为AI技术在各行业中最早实现规模化落地的场景之一。 目前，AI医疗影像产业的 ...