当前，人工智能技术加速赋能医疗卫生领域。江西省卫生健康委员会副主任孙常翔认为，人工智能与医 疗卫生深度融合，是提升医疗服务能力，推进健康江西建设的关键路径。江西省卫健委将积极探索AI 在辅助诊断、疾病筛查、医院管理、居民健康监测医学科学研究等方面的深度应用，为健康江西建设注 入智慧动能。 江西多地也纷纷抢滩"人工智能+"。省会南昌在2025年就出台了《南昌市推进"人工智能+"行动2025年工 作方案》，提出在三大领域建设17个应用场景。"稀土王国"赣州市推进人工智能与稀土产业融合发展， 让研发少走弯路、提质增效。 算力是人工智能发展的核心基础设施。行动方案提出，力争到2030年，江西智算服务规模达到 5000PFlops，构建30个以上高质量行业数据集，形成50个以上行业应用模型，新一代智能终端、智能体 等应用普及率超90%。 刘兵表示，行动方案深度结合江西重点产业链，以及数据要素、算力基础设施、行业应用场景现状，推 动"人工智能+"与江西实体经济深度融合。(完) 中新网南昌1月16日电 (记者 吴鹏泉)从工业生产到医疗养老，从文体旅游到交通运输……江西部署十二 大"人工智能+"重点行动，推进人工智能商业化规 ...

Investment Rating - The report indicates a positive investment outlook for the AI healthcare industry, highlighting significant growth potential and increasing investment activity in the sector [3][10][46]. Core Insights - The AI healthcare sector is entering a new phase characterized by multi-modal integration and practical applications, driven by advancements in large models and generative AI [3][7]. - Clinical decision support and drug development are leading the commercialization efforts, while health management remains an area with untapped potential [3][14]. - The report emphasizes the importance of third-party vendors in reducing model hallucination rates and enhancing the reliability of AI applications in healthcare [3][18][21]. Summary by Sections AI Healthcare Trends - The industry is experiencing heightened investment interest and technological advancements, with major players accelerating their presence in the AI healthcare space [9][10][13]. - The integration of AI into clinical workflows is becoming standard, with AI-assisted diagnosis systems deployed in 89% of tertiary hospitals [8][14]. AI Healthcare Application Penetration - AI applications are diversifying across clinical diagnosis, drug development, health management, and AI for science (AI4S), with varying levels of maturity and market potential [30][31]. - The report identifies key areas of application, including intelligent clinical decision support, drug discovery, and personalized health management [36][39]. Key Companies in AI Clinical Diagnosis - Companies like Tempus AI and 嘉和美康 are leading in AI-assisted clinical diagnosis, focusing on electronic medical records and data-driven healthcare solutions [47][51]. - The report highlights the importance of data barriers and scenario positioning in determining the value of AI diagnostic tools [3][46]. Key Companies in AI Health Management - Companies such as 阿里健康 and 京东健康 are leveraging AI to enhance online healthcare services, with a focus on chronic disease management and personalized health plans [39][44]. - The report notes that the integration of insurance and healthcare services is crucial for the growth of AI health management solutions [39][44]. Key Companies in AI Drug Development - The report discusses the role of AI in accelerating drug development processes, with companies like Recursion and 晶泰控股 focusing on preclinical research and drug discovery [38][44]. - AI is expected to significantly reduce the time and cost associated with traditional drug development methods [38][44]. AI4S and Broader Applications - AI4S is identified as a growing field with applications in various scientific domains, including life sciences and materials science, although it faces longer conversion cycles [42][44]. - The report emphasizes the need for innovative approaches to data generation and modeling in AI4S applications [42][44].