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].

持续推动医疗影像领域变革。 本文为IPO早知道原创 作者｜ Stone Jin 微信公众号｜ipozaozhidao 据 IPO早知道消息， 数坤科技 日前在 CMEF 上亮相了升级后的 "数坤坤" 多模态医疗健康大模型 （以下简称"数坤坤"大模型） 。 依托这一前沿大模型，数坤科技推出了数字人体 4.0技术平台以及平台之上被"数坤坤"大模型全面赋 能升级后的数智影像、数智超声、数智医院、数智基层解决方案，和一系列加载了"数坤坤"能力的AI 原生硬件。 数坤科技创始人、董事长毛新生 表示， 数字人体 4.0将带动医疗健康产业升级进入新阶段，希望通 过数字化的人体，让所有的医生在为病人服务时能拥有跟今天完全不同的智能化手段。数坤在大模型 本身以及模型应用方面，都走在全世界的前列。 作为医疗垂类大模型， "数坤坤"已经可以识别出CT、MR、DR、X-ray、钼靶以及超声等模态的影 像数据，深刻理解患者的生化检查、诊断报告、既往病史和现病史等文本信息。同时，在学习了公域 这种创新模式相当于为医生配备了处理繁琐工作的 "智能助手"，显著提升诊疗效率与质量；同时为 患者提供24小时在线的"专属医生"，实现个性化的健康管 ...

数坤科技 多模态医疗健康大模型落地深圳 近日，深圳市人民医院、数坤科技与华为三方达成深度合作，实现了DeepSeek-R1模型及"数坤坤"多模态医 疗健康大模型的院内部署，并共同打造基于医疗专业大模型的数智医院建设整体解决方案，将为医院的医 疗及科研水平提升注入强大动力，开启全院医疗智慧化的崭新篇章。 与传统的科研平台不同，搭载了DeepSeek及"数坤坤"医学大模型能力的平台将为深圳市人民医院提供全方 位的科研助力， 不仅能够对科研设计进行合理评估和建议，还能利用模型对医学数据的理解能力，快速构 建研究所需的多模态数据库 。同时，该平台为科研人员配备了丰富、适配的数据分析工具与智能建模工 具，实现近乎全自动化的医疗数据要素治理和AI科研范式执行。 神州医疗 全球首个腹膜透析大模型正式发布 3月13日，神州医疗与中山大学附属第一医院 （下简称"中山一院"） 携手发布了全球首个腹膜透析大模型。 该大模型基于先进的DHC+DeepSeek双引擎架构，结合多模态融合技术，实现了对腹膜透析领域复杂信息的 精准理解和处理。 DHC医疗垂直领域多模态基座模型可实现医疗领域的多模态数据分析，能够对临床文 本、影像、病理、基 ...