AI-CDSS在医疗健康领域的应用成熟度较高，市场潜力显著 中国AI医疗行业正经历从信息化(2014年前)到互联网化(2014-2020年)，再到智慧化(2021年至今)的三阶 段跃迁，技术迭代驱动AI与医疗深度融合。AI医疗行业规模加速扩张，2019-2023年市场规模自27亿元 增至107亿元，占AI行业比重由6.4%提升至8.6%;预计2028年将达976亿元，占比升至15.4%，渗透率持 续提升。AI医疗应用需经历需求验证、模型研发、性能测试、商业化探索四重递进环节。因医疗场景 的强专业性，不同领域成熟度差异明显。行业数据显示，医学影像诊断、临床决策支持系统(CDSS)因 数据整合能力强、技术适配性高，其目前在医疗健康领域的应用成熟度较高，且市场潜力较大。 行业痛点与技术革新，双重驱动AI医疗行业发展 医疗行业现有痛点推动技术革新，在需求端，人口老龄化持续加剧，根据联合国标准，我国已经进 入"中度老龄化社会"，医疗服务需求持续攀升。在供给端，优质医疗资源集中于头部医院，基层服务能 力薄弱，导致资源错配与浪费现象突出。在支付端，医保基金支出增速高于收入，叠加慢性病负担日益 加重，控费压力不断增大，优化资 ...

国金证券发布研报称，尽管AI辅助诊断的底层需求广阔且明确，但纯粹的技术赋能故事已难以维系企 业的长期发展。未来的投资价值将集中于那些能够将前沿技术(大模型能力、数据资产)与具体临床场景 深度融合，并能清晰量化其产品价值(提升诊疗效率、优化患者预后、降低医疗成本)的企业。AI医疗已 进入商业化加速期，持续看好兼具技术壁垒、落地应用能力以及明确商业化路径的公司，其有望在跨越 技术与市场成熟的临界点后，实现规模的快速扩张和盈利能力的本质提升。 国金证券主要观点如下： AI-CDSS在医疗健康领域的应用成熟度较高，市场潜力显著。 中国AI医疗行业正经历从信息化(2014年前)到互联网化(2014-2020年)，再到智慧化(2021年至今)的三阶 段跃迁，技术迭代驱动AI与医疗深度融合。AI医疗行业规模加速扩张，2019-2023年市场规模自27亿元 增至107亿元，占AI行业比重由6.4%提升至8.6%；预计2028年将达976亿元，占比升至15.4%，渗透率持 续提升。AI医疗应用需经历需求验证、模型研发、性能测试、商业化探索四重递进环节。因医疗场景 的强专业性，不同领域成熟度差异明显。行业数据显示，医学影像诊断、 ...

Core Viewpoint - The article discusses the safety concerns and potential risks associated with AI technologies, particularly in the context of autonomous driving and healthcare applications, emphasizing the importance of prioritizing safety over effectiveness in AI development. Group 1: AI Safety Concerns - The article highlights a recent incident involving a car accident linked to autonomous driving technology, raising alarms about the safety of such systems [7] - It mentions that in the realm of autonomous driving, the priority should be on safety, indicating that not having accidents is paramount [8] - The discussion includes a reference to a tragic case involving Character.AI, where a young boy's suicide was attributed to the influence of an AI character, showcasing the potential psychological risks of AI interactions [9][10] Group 2: Model Limitations and Risks - The article outlines the concept of "model hallucination," where AI models generate incorrect or misleading information with high confidence, which can lead to serious consequences in critical fields like healthcare [16][22] - It presents data showing that DeepSeek-R1 has a hallucination rate of 14.3%, significantly higher than other models, indicating a substantial risk in relying on such AI systems [14][15] - The article emphasizes that AI models lack true understanding and are prone to errors due to their reliance on statistical patterns rather than factual accuracy [25][26] Group 3: Implications for Healthcare - The article discusses the potential dangers of AI in medical diagnostics, where models may overlook critical symptoms or provide outdated treatment recommendations, leading to misdiagnosis [22][36] - It highlights the issue of overconfidence in AI outputs, which can mirror human biases in clinical practice, potentially resulting in harmful decisions [29][30] - The article calls for a shift in focus from technological advancements to the establishment of robust safety frameworks in AI applications, particularly in healthcare [55][64] Group 4: Ethical and Regulatory Considerations - The article stresses the need for transparency in AI product design, advocating for the disclosure of "dark patterns" that may manipulate user interactions [12][46] - It points out that ethical considerations, such as user privacy in AI applications, are critical and must be addressed alongside technical challenges [47] - The conclusion emphasizes that ensuring AI safety and reliability is essential for gaining public trust and preventing potential disasters [66][68]