自1956年人工智能（artificial intelligence，AI）概念提出以来，医学始终是其核心应用领域之一。当前，AI技术已贯穿诊疗全流程，并延伸至药物研发、手术机器人及临床试验优化等创新场景，形成以数据治理、算法创新、场景应用为支柱的技术体系。多模态数据融合整合影像、病历、基因等异构数据，联邦学习实现跨机构隐私保护共享；深度学习在影像诊断中实现90%以上的肺结节检测灵敏度；生成式AI加速药物分子设计。核心应用覆盖四大领域：AI在乳腺癌、糖尿病视网膜病变筛查中准确率超专业医师；机器人技术缩短住院时间并提高脊柱螺钉植入精度；AI缩短药物靶点的发现周期；机器学习将受试者提升筛选效率，并实现实时数据监测。AI在医疗领域的应用首先受到数据质量和算法偏差的制约，AI模型的“黑箱”特性和责任归属模糊是临床落地的核心障碍。本文通过分析关键技术突破和典型案例，探讨了AI在医学中的应用场景及其面临的挑战，旨在为医疗智能化的未来发展提供参考。

Since the concept of artificial intelligence （AI） was proposed in 1956， medicine has been one of its core application fields. At present， AI technology has run through the whole diagnosis and treatment process， and has been extended to innovative scenarios such as drug research and development， surgical robots， and clinical trial optimization. Scenario application is the backbone of the technical system. Multimodal data fusion integrates heterogeneous data such as images， medical records， and genes， and federated learning realizes cross-institutional privacy protection and sharing. Deep learning achieved more than 90% sensitivity in imaging diagnosis for lung nodule detection， while generative AI accelerates drug molecule design. The core applications cover four major areas field： AI is more accurate than professional doctors in breast cancer and diabetic retinopathy screening； robotics shortens hospital stays and improves spinal screw placement accuracy； AI shortens the discovery cycle of drug targets； machine learning improves the efficiency of subject screening and enables real-time data monitoring. The application of AI in the medical field is first constrained by data quality and algorithm bias， and the “black box” characteristics of AI models and the ambiguity of responsibility attribution are the core obstacles to clinical implementation. This paper analyzes key technological breakthroughs and typical cases， discusses the application scenarios and challenges of AI in medicine， and aims to provide a reference for the future development of medical intelligence.