作者 | 棱镜 AI 浪潮席卷之下，企业技术领袖们无不摩拳擦掌，渴望将这些颠覆性技术融入自身的业务核心，抢占智能时代的制高点，不料却被现实狠狠地甩下一记 耳光。 PoC 时的惊艳还历历在目——自动报告生成、智能应答客服、代码辅助开发，一切都看起来那么完美。然而，当试图将这些能力嵌入核心业务系统时， 医疗团队发现，AI 助手会面不改色地编造根本不存在的药物方案；金融机构意识到，风控模型可能依据过时的条款做出百万级的误判；连最简单的客服 场景中，AI 都能把用户引导向一个早已下架的产品。这并非由于某个技术事故，而是生成式 AI 存在已久的幻觉问题。 清华大学计算机系教授指出，大模型在垂直领域知识与实时更新上是有局限的，特别是幻觉问题，已经成为大模型深入企业级应用的掣肘。因此，行业 迫切需要一种既保留大模型生成能力，又能对其输出进行确定性约束的方案。 9 月 25 日，由李国良教授作为技术顾问的数智引航团队，正式发布向量数据库 VexDB，能够支持百亿千维向量数据毫秒级查询，召回准确度高达 99% 以上，从数据基础设施层面为 AI 应用构建一个可信的知识基石。近日，在国际权威的 DABSTEP 非结构化数据分析测试 ...

Core Viewpoint - The integration of AI in medical imaging is advancing rapidly, with large models evolving from mere tools to core drivers of diagnostic ecosystems, enhancing the workflow of radiologists and addressing challenges in pathology diagnostics [1][2]. Group 1: Development of AI in Medical Imaging - Medical imaging AI models have achieved widespread application in the workflow of radiologists, transitioning from auxiliary diagnostic tools to essential components of the diagnostic ecosystem [1]. - The "Shukun Kun Multi-modal Medical Health Large Model" released by Shukun Technology in April signifies this evolution, enhancing the role of AI in diagnostics [1]. Group 2: Challenges and Solutions in Pathology - Pathology models are considered the "crown jewel" of medical models due to their complexity and diversity, with the first clinical-grade pathology model, "Insight," developed by Tuo Che Future, addressing accuracy and efficiency challenges [2]. - The pathology model addresses long-standing challenges in generalization across hospitals, cancer types, and pathology tasks, simplifying processes and improving diagnostic efficiency [3]. Group 3: Enhancing AI Generalization Performance - AI model generalization is crucial for reliability and stability, with key challenges including insufficient data diversity, model limitations, and the long-tail nature of medical data [4][6]. - Strategies to enhance generalization include expanding data sample diversity, optimizing model training, and iterating models in real clinical environments [6][7]. Group 4: Addressing the Hallucination Problem - The hallucination issue in large models is a significant barrier, with RAG (Retrieval-Augmented Generation) technology proposed as a solution to enhance accuracy by integrating external knowledge [8][9]. - A hybrid approach combining generative and discriminative AI is suggested to mitigate risks in critical decision-making scenarios, ensuring reliable outputs [9]. Group 5: Deployment Trends in Healthcare - Local deployment of AI models is becoming the preferred choice for hospitals due to data privacy and compliance advantages, with integrated solutions like one-box systems gaining traction [10][11]. - One-box systems combine the strengths of general and specialized models, addressing diverse medical needs while ensuring data control [10]. Group 6: Future Trends in Medical AI - The performance of medical large models is surpassing traditional small models, with applications expanding from thousands to over ten thousand hospitals [12]. - The future of medical AI is moving towards multi-modal integration and comprehensive diagnostics, akin to a digital "general practitioner" that synthesizes various patient data for holistic treatment recommendations [12][13].