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京东健康发布京医千询3.0与MedWork 医疗AI从“回答问题”转向“交付结果”

亿邦动力 2026-09-14 16:50
亿邦动力 2026/09/14 16:50

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你可快速掌握京东健康最新上线的AI健康服务核心信息,获取能直接使用的实用医疗服务干货。

1. 升级后的京东健康APP定位“国民首席AI健康管家”,可绑定血糖仪、血压计等家用监测设备自动同步健康数据,联动在线问诊、到家快检、护士上门等服务,把口头健康建议转化为可落地的上门服务;服务填补了凌晨非门诊时段、三线及以下区域的传统医疗空白,16.77%的AI咨询发生在凌晨,55.69%的AI用户来自三线及以下城市。

2. 全智能名医智能体可解决普通用户看病的四类常见痛点:帮助判断是否需要就医、推荐对应就诊科室、解读检查报告、提醒按时复诊,系统会按病情风险自动分流,轻症由AI提供服务、重症转线上专家问诊、高危情况直接提示立即就医。

3. 慢病出院患者可通过平台实时同步健康数据,由医生根据数据动态调整诊疗方案,不用被动等待线下复诊。

本次京东健康发布的医疗AI生态布局,清晰传递了健康赛道的消费趋势、合作方向与产品研发参考。

1. 消费趋势层面,AI医疗正在填补传统医疗的两大服务空白,即凌晨时段的即时健康需求、三线及以下城市的下沉健康需求;用户对健康管理的期待已经从单一的问诊咨询,转向“监测-干预-问诊-康复”的全闭环服务,慢病院外长期管理是需求增长最快的场景。

2. 生态合作层面,京东健康已和鱼跃、欧姆龙、瑞思迈、可孚等品牌完成AI+医疗器械战略签约,合作核心是将硬件监测数据接入个人健康档案,联动问诊、上门护理等服务形成服务闭环。

3. 品牌营销层面,平台沉淀的3500余个覆盖400余家三甲医院的专家智能体、超百万服务医生网络,可为合作品牌提供专业信任背书,帮助品牌触达更广泛的下沉与全时段需求用户。

京东健康本次披露的医疗AI体系布局,释放了健康消费赛道明确的增长机会、可复用商业模式与合作方向。

1. 增量市场机会清晰,两大空白场景待挖掘:一是凌晨时段的即时健康咨询与服务需求,占整体AI咨询量的16.77%;二是三线及以下城市的下沉健康服务市场,占AI服务用户的55.69%,其中家用医疗器械配套服务、慢病院外管理是核心增长点。

2. 可直接借鉴的新商业模式为“AI+硬件+服务”闭环,不再单一售卖产品,而是将硬件监测数据对接AI健康管理系统,联动在线问诊、上门护理、耗材配送做长期用户运营,比如慢阻肺患者出院后呼吸机配送到家+护士上门调试+数据同步医生调方案的模式,用户粘性与复购率远高于单次卖货。

3. 合作提示:当前京东健康正开放AI+医疗器械生态合作,接入商家可共享平台AI能力、专家资源、配送及上门服务网络,可重点关注相关合作入口。

京东健康发布的AI医疗生态布局,为医疗健康类生产工厂明确了产品设计方向、商业合作机会与数字化转型启示。

1. 产品设计新需求明确:未来血糖仪、血压计、呼吸机、动态血糖监测仪等家用医疗器械,需要适配AI健康管理的数据打通要求,支持监测数据自动同步至个人健康档案,配合AI完成风险识别、异常预警,跳出单一监测功能的同质化竞争。

2. 商业合作机会可观:当前京东健康正推进AI+医疗器械生态签约,已接入多个头部器械品牌,合作工厂可共享平台AI服务能力、到家配送、护士上门、互联网问诊的服务网络,覆盖院外慢病管理、下沉市场、凌晨应急需求等增量场景。

3. 数字化转型启示:工厂可依托平台的AI用户需求洞察,围绕体重管理、呼吸慢病管理等细分场景做定制化产品研发,同时对接医院端AI哨点系统的采购需求,打开C端消费与B端医用的双向市场。

京东健康本次发布的医疗AI技术矩阵与落地路径,清晰展现了医疗AI服务行业的发展趋势、技术方向与客户痛点解决方案。

1. 行业发展趋势明确:医疗AI已经度过早期“回答问题”的内容生成阶段,正式转向“交付结果”的任务执行阶段,只有真正嵌入医生日常工作流、用户就医全流程、健康管理全闭环的AI服务,才具备规模化落地价值。

2. 核心技术落地方向清晰:底层大模型需要具备原生3D影像理解、临床指南代码化可溯源、长程任务跨工具协作三大能力,上层要搭建支持多端适配、全流程任务执行的工作台,以及覆盖问诊全流程的智能体产品。

3. 痛点解决方案明确:针对医生端,AI可承担阅片预处理、科研资料整理、患者数据追踪等步骤化工作,保留医生的临床决策权,提升工作效率;针对用户端,通过AI分层分流+硬件联动的方式,解决就医决策难、报告看不懂、服务覆盖不足的痛点。

京东健康本次升级的全链路AI健康服务体系,为健康类平台的服务迭代、运营管理、招商布局与风险规避提供了可参考的成熟实践。

1. 平台服务升级方向:可将原有平台功能与任务执行型医疗大模型打通,一方面搭建嵌入医生日常工作流的AI工作台,降低医生使用门槛;另一方面推出覆盖问诊全流程的智能体,解决用户从就医决策到康复随访的全链路痛点。

2. 运营管理可借鉴做法:打通家用硬件数据、在线问诊、到家快检、护士上门、慢病随访的全链路服务,重点覆盖凌晨非门诊时段、三线及以下下沉市场的空白需求;同时联动三甲医院专家共建标准化智能体,目前京东健康已建成超3500个专家智能体,覆盖400余家三甲医院,服务好评率超96%。

3. 招商与风险规避:重点开放AI+医疗器械的生态合作,引入符合数据打通要求的合作方;同时建立严格的病情分层机制,高危病情第一时间提示用户就医,明确AI辅诊的定位,规避诊疗合规风险。

京东健康集中发布的系列医疗AI成果,清晰展现了国内医疗AI产业落地的最新动向、模式创新与值得研究的新命题。

1. 产业技术新动向:医疗大模型已经从“生成答案”的内容输出阶段,正式迈入“交付结果”的任务执行阶段,核心技术能力覆盖原生3D影像理解、临床指南代码化可溯源、长程跨工具任务协作;本次发布的京医千询3.0在正高职称考试、权威医学评测、3D影像识别、医学论文生成等场景均有公开可查的量化效果数据。

2. 商业模式创新:一是推出嵌入医生全场景工作流的AI工作台模式,通过人机协同完成阅片、科研写作、辅诊、院外患者管理等复杂医学任务;二是打造“AI+硬件+服务”的健康管理闭环,从单一咨询走向监测、干预、问诊、康复全链路服务,目前已在温州医科大学附一院风险筛查、慢阻肺居家管理等场景落地。

3. 待研究的新方向:包括AI医疗的责任边界划分、诊疗结果的全链路溯源机制、AI对医疗服务公平性的提升作用、专家诊疗经验沉淀为模型能力的标准化路径等。

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Quick Summary

This summary breaks down the key details of JD Health’s newly launched AI health services, with actionable, practical information for everyday users.

1. The upgraded JD Health app is positioned as the public’s leading AI health steward. It can sync real-time health data from connected home monitoring devices such as glucometers and blood pressure monitors, and integrate these readings with services including online consultations, at-home rapid testing, and home visits from nurses, turning generic health advice into actionable, on-site care. The service fills longstanding gaps in traditional care: 16.77% of all AI consultations take place during late-night hours when regular clinics are closed, and 55.69% of AI service users are based in third-tier and lower cities, where medical access has historically been limited.

2. Its fully AI-powered specialist medical agent addresses four common pain points for ordinary patients: helping users judge whether they need to seek in-person care, recommending the appropriate clinical department, interpreting medical test reports, and sending reminders for scheduled follow-up visits. The system automatically triages cases by risk level: AI handles mild conditions, moderate cases are routed to online specialist consultations, and high-risk cases receive an immediate prompt to seek emergency medical care.

3. Patients managing chronic conditions post-discharge can sync their health data to the platform in real time, allowing doctors to adjust treatment plans dynamically without requiring patients to wait for scheduled in-person follow-up appointments.

JD Health’s recently unveiled medical AI ecosystem layout offers clear signals for consumer trends, partnership pathways, and product R&D priorities in the healthcare sector.

1. On the consumer trend front, AI-powered healthcare is filling two core gaps in traditional care: immediate late-night health demand, and unmet needs in third-tier and lower-tier sinking markets. User expectations for health management have shifted from standalone consultation services to full closed-loop care covering monitoring, intervention, consultation, and rehabilitation, with long-term out-of-hospital chronic disease management representing the fastest-growing demand segment.

2. For ecosystem partnerships, JD Health has signed strategic AI + medical device agreements with leading brands including Yuwell, Omron, ResMed, and Cofoe. The core of these partnerships is to integrate hardware monitoring data into personal health records, and connect the data to services such as consultations and at-home nursing to form a closed service loop.

3. For brand marketing, the platform’s more than 3,500 specialist AI agents covering over 400 Class A tertiary hospitals, plus its network of over 1 million licensed service doctors, can provide professional credibility backing for partner brands, helping them reach a broader base of users across sinking markets and 24/7 demand scenarios.

JD Health’s disclosed medical AI system layout points to clear growth opportunities, replicable business models, and cooperation pathways in the health consumer sector.

1. Incremental market opportunities are well-defined across two underserved scenarios: first, immediate late-night health consultation and service demand, which accounts for 16.77% of total AI consultation volume; second, the sinking health service market in third-tier and lower cities, which makes up 55.69% of AI service users. Supporting services for home medical devices and out-of-hospital chronic disease management are the core growth drivers in these segments.

2. A directly replicable new business model is the closed "AI + hardware + service" loop: rather than selling products as one-off transactions, sellers can connect hardware monitoring data to the AI health management system, and integrate online consultations, at-home nursing, and consumable delivery for long-term user operation. For example, for COPD patients post-discharge, a package of at-home ventilator delivery, in-home nurse setup, and real-time data syncing for doctors to adjust treatment plans delivers far higher user stickiness and repurchase rates than standalone product sales.

3. Cooperation note: JD Health is currently opening up its AI + medical device ecosystem partnerships. Merchants that join the ecosystem can access the platform’s AI capabilities, expert resources, delivery network, and at-home service network, with relevant cooperation entry points worth prioritizing.

JD Health’s AI medical ecosystem layout provides clear direction for product design, commercial partnership opportunities, and digital transformation takeaways for medical and health product manufacturers.

1. New product design requirements are clearly defined: future home medical devices including glucometers, blood pressure monitors, ventilators, and continuous glucose monitors will need to support data connectivity for AI health management, enabling automatic syncing of monitoring data to personal health records, and collaborating with AI for risk identification and abnormal alerts, to break away from homogeneous competition based solely on monitoring functionality.

2. Commercial partnership opportunities are substantial: JD Health is currently advancing AI + medical device ecosystem signings, with multiple leading device brands already onboarded. Partner factories can access the platform’s AI service capabilities, last-mile delivery network, home nursing services, and internet consultation network, covering incremental scenarios such as out-of-hospital chronic disease management, sinking markets, and late-night emergency demand.

3. Digital transformation takeaways: Factories can leverage the platform’s AI-powered user demand insights to develop customized products for niche segments such as weight management and chronic respiratory disease management, while also tapping into procurement demand for hospital-side AI sentinel systems, opening up dual growth paths in both consumer-facing and institutional medical markets.

JD Health’s newly released medical AI technology matrix and implementation roadmap clearly illustrate industry development trends, technical priorities, and user pain point solutions for the medical AI service sector.

1. Industry development trends are unambiguous: Medical AI has moved past the early generative stage of "answering questions" and formally entered the task execution stage of "delivering outcomes." Only AI services that are truly embedded into doctors’ daily workflows, users’ full care-seeking journeys, and the closed loop of end-to-end health management have scalable implementation value.

2. Core technology implementation priorities are clear: The underlying large model needs three core capabilities: native 3D medical image understanding, codified and traceable clinical guideline alignment, and cross-tool collaboration for long-running tasks. The upper application layer requires a multi-device compatible workbench for full-process task execution, plus AI agent products covering the entire consultation workflow.

3. Pain point solutions are well-defined: For doctors, AI can take on step-by-step work including imaging pre-processing, research material organization, and patient data tracking, preserving doctors’ clinical decision-making authority while boosting work efficiency. For users, a combination of AI-based triage and hardware integration addresses core pain points including difficulty making care-seeking decisions, inability to understand test reports, and insufficient service coverage.

JD Health’s upgraded full-link AI health service system provides a mature, replicable reference for service iteration, operational management, merchant recruitment, and risk mitigation for health-focused digital platforms.

1. Platform service upgrade direction: Platforms can integrate existing functions with task-execution medical large models: on one hand, build AI workbenches embedded in doctors’ daily workflows to lower adoption barriers for clinicians; on the other hand, launch full-consultation-journey AI agents to address user pain points across the entire chain from care-seeking decisions to rehabilitation follow-up.

2. Actionable operational management practices: Connect full-link services across home hardware data, online consultations, at-home rapid testing, home nursing visits, and chronic disease follow-up, with a focus on covering unmet demand during late-night non-clinic hours and in third-tier and lower-tier sinking markets. At the same time, co-build standardized AI agents with Class A tertiary hospital experts; to date, JD Health has built over 3,500 specialist AI agents covering more than 400 Class A tertiary hospitals, with a service satisfaction rate exceeding 96%.

3. Merchant recruitment and risk mitigation: Prioritize opening up AI + medical device ecosystem partnerships, and onboard partners that meet data connectivity requirements. At the same time, establish a strict condition stratification mechanism that immediately prompts users with high-risk conditions to seek in-person care, clearly defining AI’s role as an auxiliary diagnostic tool to mitigate clinical compliance risks.

JD Health’s series of concentrated medical AI releases clearly demonstrate the latest industrial implementation trends, model innovations, and emerging research-worthy propositions in China’s medical AI sector.

1. New industrial technology trends: Medical large models have officially moved from the content output stage of "generating answers" to the task execution stage of "delivering results," with core technical capabilities covering native 3D image understanding, codified and traceable clinical guideline alignment, and cross-tool collaboration for long-running tasks. The newly released Jingyi Qianxun 3.0 model has publicly verifiable quantitative performance data across scenarios including senior professional title medical exams, authoritative medical evaluations, 3D image recognition, and medical paper generation.

2. Business model innovations: First, the platform launched an AI workbench model embedded across all scenarios of doctors’ workflows, enabling human-AI collaboration to complete complex medical tasks including imaging reading, research writing, auxiliary diagnosis, and out-of-hospital patient management. Second, it built a closed "AI + hardware + service" health management loop, moving from standalone consultation to full-link services covering monitoring, intervention, consultation, and rehabilitation, which has been deployed in scenarios including risk screening at the First Affiliated Hospital of Wenzhou Medical University and home-based COPD management.

3. Emerging research directions include the delineation of liability boundaries for AI-powered healthcare, full-link traceability mechanisms for diagnostic and treatment outcomes, AI’s role in improving equity in medical service access, and standardized pathways for codifying specialists’ clinical expertise into model capabilities.

Disclaimer: The "Quick Summary" content is entirely generated by AI. Please exercise discretion when interpreting the information. For issues or corrections, please email run@ebrun.com .

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【亿邦原创】在日前举行的JDDiscovery-2026京东全球科技探索者大会上,京东健康集中发布了一系列医疗AI成果,包括京医千询3.0大模型、MedWork医生AI工作台,以及业内首个全智能名医智能体。京东健康APP同步升级为“国民首席AI健康管家”,并完成多项AI+医疗器械生态合作签约。

京医千询3.0:从“生成答案”到“执行任务”

京医千询3.0的核心变化,是让医疗AI从“生成答案”走向高质量的“执行任务”。三项能力支撑了这一跨越:原生3D影像理解,学习医生真实阅片轨迹,能主动调整窗宽、定位病灶、对比时序影像;MedCode代码执行模式,把临床指南转化为可执行、可校验的高可靠代码,每一条结论都能溯源;长程任务协作,支持跨步骤调用工具,完成多学科会诊、论文写作等复杂工作。

京东健康方面表示,在多项权威医学评测中,京医千询3.0正高职称考试十门平均72.3分,HealthBench Hard得分50.6,MedBench v5得分75.2,3D影像评测准确率82%,医学论文写作一次生成无差错率达到99%。

MedWork医生AI工作台:让AI能力进入医生日常工作流

基于京医千询3.0,京东健康正式发布医生AI工作台MedWork。如果说京医千询3.0解决的是模型如何理解、查证和执行复杂医学任务,MedWork解决的则是这些能力如何进入医生每天的工作。它把任务目标、病例与研究资料、工具执行过程和最终交付物放在同一个工作空间里,支持移动端、桌面端和PC端,并可按场景使用云端或本地工作空间。

MedWork面向的不是一次问答,而是一项需要连续执行数百个步骤的完整医学任务。以医学论文为例,研究者给出研究问题和原始数据后,MedWork可以连续完成研究现状梳理、文献检索和证据核验、实验方案设计、分析代码编写与运行、统计检验、结果复核及图表绘制,再按照目标期刊或既有模板组织摘要、方法、结果、讨论和参考文献,最终交付论文稿、图表、分析代码及可复盘的执行过程。研究者和医学专家可以在任何环节查看依据、修正方向或接管任务。

在AI 3D阅片任务中,MedWork将完整CT、MRI等三维影像载入医生工作空间,调用京医千询3.0的原生3D理解能力,按照医生真实阅片方式连续浏览影像序列、调整窗宽窗位、切换平面、定位和测量病灶,并对比不同时点的影像变化。医生能够实时看到AI正在查看的位置、执行的操作及对应依据;发现资料冲突或判断偏差时,可以立即中断并返回原始影像复核。AI负责影像资料处理、可疑发现定位和辅助分析,医生负责临床判断与结果采纳。

全智能名医智能体:覆盖问诊全流程

京东健康发布了业内首个全智能名医智能体,目标是接住用户看病过程中的四个常见痛点:不知道该不该去医院,不知道挂哪个科,检查报告看不懂,看完病没人提醒复诊。

用户输入症状、病史、检查报告后,系统会结构化收集信息,生成病情摘要,并按风险等级自动分流:轻的走智能体服务,重的转专家线上问诊,高危的优先提示立即就医。进入智能体后,用户获得四项核心服务:病情分析、报告解读、就医建议、康复随访。在医生端,它化身辅诊助手,帮助补全病情、提供鉴别诊断、预警用药风险。

这项能力源于专家共建。首都医科大学附属北京安定医院临床心理中心首席专家李占江教授在论坛现场表示:“可用、好用、敢用。这是医生对AI最直接的判断。”目前,京东健康正与中南大学湘雅二院王小平教授、首都医科大学附属北京安定医院李占江教授、中日友好医院杨顶权教授等专家深度合作,把专家的诊疗思路、表达方式和服务标准沉淀为可复用的模型能力。京东健康医生专家智能体已超3500个,覆盖400余家三甲医院,好评率超96%。

据京东健康方面的数据,在医生日常工作中,京东知医深度整合超5000万份医学文献、临床指南及权威期刊资源,已服务全国超百万医生,辅助诊疗决策超2000万次。

“AI+硬件+服务”:从“测”到“管”的闭环

京东健康APP升级为“国民首席AI健康管家”,把血糖仪、血压计等设备的监测数据纳入个人健康档案,联动在线问诊、到家快检、护士上门,让健康管理从口头建议变成真正送到身边的服务。论坛现场,京东健康与鱼跃、欧姆龙、瑞思迈、可孚、硅基动感、新华医疗完成AI+医疗器械战略签约。

面向用户的AI医生“大为”,使用用户数同比增长5倍。两个数据值得关注:16.77%的咨询发生在凌晨,55.69%的用户来自三线及以下城市。AI正在填补传统医疗服务最难覆盖的两个空白——时间和地域。

在医院场景,京东卓医以“AI+供应链”支撑体重管理全流程:医院定方案,AI持续服务,供应链支持执行。患者离开医院后,服务仍在继续——拍照分析餐食、一周回顾、AI建议,医生端可一键查看患者数据。在温州医科大学附属第一医院,AI哨点系统已覆盖超245万人次到院患者,对体重及代谢风险人群进行自动识别和分层。

论坛圆桌分享了一个真实案例:一位慢阻肺合并心梗的老人,出院后血氧只有90%。通过互联网医院视频问诊,第二天呼吸机送到家,护士上门调试,数据实时同步,医生动态调整方案。中日友好医院林江涛教授从临床角度对这一模式表示了认可:“慢阻肺患者出院后的长期管理一直是个大难题。现在通过数据回传和远程问诊,医生从被动等患者复诊,变成主动根据数据调整方案。这对慢病管理来说,是一次真正的模式升级。”

亿邦持续追踪报道该情报,如想了解更多与本文相关信息,请扫码关注作者微信。

本文首发于 亿邦动力 官方网站

文章来源:亿邦动力

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FAQ回顾

京医千询3.0是什么?

京医千询3.0是京东健康发布的医疗AI大模型,核心定位是从“生成答案”转向高质量“执行任务”,具备原生3D影像理解、MedCode代码执行、长程任务协作三项核心能力,在多项权威医学评测中表现优异,3D影像评测准确率达82%,医学论文写作一次生成无差错率99%。

MedWork医生AI工作台能为医生提供什么帮助?

MedWork是京东健康基于京医千询3.0打造的医生AI工作台,支持移动端、桌面端、PC端多端使用,可将任务目标、病例资料、工具执行过程、最终交付物整合到统一工作空间,辅助医生连续完成医学论文写作、3D阅片等复杂任务,医生可随时介入复核、调整方向。

全智能名医智能体可以解决哪些就医痛点?

京东健康推出的业内首个全智能名医智能体可覆盖问诊全流程,解决用户不知是否该就医、不知挂什么科、看不懂检查报告、无人提醒复诊四大常见痛点,可按病情风险自动分流,提供病情分析、报告解读、就医建议、康复随访服务,同时可辅助医生开展诊疗工作。

AI技术给慢病管理模式带来了哪些改变?

AI可打通“AI+硬件+服务”的健康管理闭环,填补传统医疗在凌晨时段、下沉市场的服务空白,针对慢阻肺等慢病院外管理场景,可实现患者健康数据实时回传,支持医生远程动态调整诊疗方案,将传统被动等患者复诊的模式转为主动干预。

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