在全球医疗健康体系向智能化升级的浪潮中，人工智能（Artificial Intelligence, AI）医疗正处于由技术验证走向场景落地、由碎片化试点走向系统化推进的关键阶段。AI医疗并非单一技术应用，而是以大模型、多模态数据融合和医学知识增强为底座，与临床诊断、疾病治疗、康复与慢病管理、医院运营、支付保险、科研及药物研发等场景深度融合，推动医疗服务向智能化、精准化和协同化转型。当前，随着医疗数据积累提速、算法能力跃升、算力基础夯实及监管政策逐步完善，AI医疗的价值边界持续拓宽，不仅提升单环节效率，也在优化资源配置、重构诊疗流程、完善产业协作生态和创新服务模式方面释放长期影响。未来，行业竞争将从单一算法能力比拼，转向数据闭环、模型迭代、场景适配、系统集成、合规治理与商业化落地等综合能力竞争。
Amid the global shift toward intelligent healthcare systems, Artificial Intelligence (AI) in healthcare is moving from technical validation to real-world implementation, and from fragmented pilots to systematic deployment. Built on foundation models, multimodal data integration, and medical knowledge enhancement, AI in healthcare is increasingly integrated into clinical diagnosis, disease treatment, rehabilitation and chronic disease management, hospital operations, payment and insurance, medical research, and drug development, driving healthcare services toward greater intelligence, precision, and coordination.
As medical data accumulates faster, algorithms advance, computing infrastructure improves, and regulatory frameworks become more developed, the role of AI in healthcare continues to expand. Its value is no longer limited to improving efficiency in individual processes, but also lies in optimizing resource allocation, reshaping diagnostic and treatment workflows, strengthening collaboration across the healthcare ecosystem, and enabling innovative service models.
Looking ahead, competition in the AI healthcare industry will shift from single-algorithm capabilities to broader strengths in data feedback, model iteration, clinical adaptation, system integration, compliance governance, and commercialization. Through this Blue Paper, Frost & Sullivan aims to provide industry participants with a systematic reference for understanding the current landscape and future trends of the global AI healthcare industry.
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沙利文 毛先生
E-mail: fred.mao@frostchina.com
