Dyania's Synapsis AI, which reasons through a patient's complete medical history, uses NVIDIA accelerated computing, so health systems can learn from their own records while keeping patient records within their walls.

Dyania 的 Synapsis AI 能够基于患者的完整病史进行推理，并采用 NVIDIA 加速计算技术，使医疗系统能够在将患者数据保留在本地的前提下，从自身记录中学习。

JERSEY CITY, N.J.,

新泽西州泽西市，

Nov. 13, 2026

2026年11月13日

/PRNewswire/ --

/美通社/ --

Dyania Health

达亚尼亚健康

today announced a collaboration with

今日宣布与

NVIDIA

英伟达

to put AI clinical reasoning to work inside health systems, on their own infrastructure, at the scale of their entire patient population. Dyania's Synapsis AI reads a patient's full medical record, across years of notes, operative reports, imaging reports and lab results, and answers clinical questions with evidence from the chart.

将人工智能临床推理应用于医疗系统内部，在其自有基础设施上，以覆盖其全部患者群体的规模进行部署。Dyania 的 Synapsis AI 能够读取患者跨越多年的完整病历，包括病程记录、手术报告、影像学报告和实验室检查结果，并依据病历中的证据回答临床问题。

NVIDIA accelerated computing makes it possible to do that across large patient populations behind the hospital firewall, without transferring patient records outside the health system. Together, the companies aim to give health systems the ability to learn from the full histories of the patients they have treated and turn that experience into earlier identification of care needs, more informed treatment decisions and better care for the next patient, while keeping those records within their own systems..

NVIDIA 加速计算使得在医院防火墙内针对大规模患者群体进行此类操作成为可能，而无需将患者病历传输到医疗系统之外。两家公司携手致力于赋予医疗系统从其所治疗患者的完整病史中学习的能力，并将这些经验转化为对护理需求的更早识别、更明智的治疗决策以及为下一位患者提供更优质的护理，同时确保这些病历数据保留在医疗系统内部。

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Dyania Patient Journey

Dyania 患者旅程

Turning clinical experience into better care

将临床经验转化为更优质的护理

Health systems hold decades of clinical experience in their medical records, but much of it is difficult to analyze at scale. Much of the information that matters is in narrative text rather than structured fields, and interpreting it often requires manual review. Registries, outcomes studies and predictive models can be limited by how many charts a team can read by hand.

医疗系统在其病历中积累了数十年的临床经验，但其中大部分数据难以进行大规模分析。许多关键信息以非结构化的叙述性文本形式存在，而非位于结构化字段中，因此解读这些信息往往需要人工审阅。登记系统、结局研究和预测模型的有效性常受限于团队能够手工阅读的病历数量。

Extracting and interpreting the clinical information needed to build them remains a major bottleneck..

提取和解读构建它们所需的临床信息仍然是一个主要瓶颈。

Synapsis AI performs that reasoning. It is built to work the way a specialist works through a chart: longitudinally, across every document, resolving what a patient actually had, when, and with what evidence. Doing this for one patient is a reasoning problem. Doing it for an entire health system, continuously, inside the hospital's own environment, requires substantial computing capacity.

Synapsis AI 执行这一推理过程。它的设计旨在模拟专家审阅病历的方式：纵向贯穿所有文档，厘清患者究竟患有何种疾病、发病时间以及相应的证据。对单个患者进行此类分析是一个推理问题；而要持续地在医院自有环境中对整个医疗系统进行此类分析，则需要强大的计算能力。

Dyania brings the clinical reasoning engine and implementation experience; NVIDIA brings the accelerated computing platform and the software that lets it run securely and efficiently on premises..

Dyania 提供临床推理引擎和实施经验；NVIDIA 提供加速计算平台以及使其能够在本地安全高效运行的软件。

Synapsis AI is built on the NVIDIA AI Enterprise software stack end to end. Dyania trains its proprietary clinical models with the NVIDIA NeMo framework for model training and reinforcement learning. Inside the health system, Synapsis AI runs on NVIDIA TensorRT and TensorRT-LLM on the hospital's own NVIDIA accelerated computing infrastructure, so inference stays inside the hospital's private network..

Synapsis AI 基于 NVIDIA AI Enterprise 软件栈端到端构建。Dyania 利用 NVIDIA NeMo 框架对其专有临床模型进行训练和强化学习。在医疗系统内部，Synapsis AI 依托医院自有的 NVIDIA 加速计算基础设施，通过 NVIDIA TensorRT 和 TensorRT-LLM 运行，从而确保推理过程始终保持在医院的私有网络内。

Under the collaboration, the companies will work to bring Synapsis AI on NVIDIA infrastructure to health systems in the U.S. and internationally, publish joint case studies, and integrate Synapsis AI with NVIDIA's healthcare software stack. Areas the companies are evaluating include NVIDIA Confidential Computing for training and inference, NVIDIA Dynamo for distributed inference serving, Multi-Instance GPU for partitioning GPUs across concurrent clinical workloads, and scaling to many NVIDIA Blackwell Ultra GPUs as deployments move from batch processing toward near-real-time clinical insight at the point of care..

在此次合作中，双方公司将致力于把基于 NVIDIA 基础设施的 Synapsis AI 引入美国及全球各地的医疗健康系统，发布联合案例研究，并将 Synapsis AI 与 NVIDIA 的医疗健康软件栈进行集成。双方正在评估的领域包括：用于训练和推理的 NVIDIA 机密计算、用于分布式推理服务的 NVIDIA Dynamo、用于在并发临床工作负载之间划分 GPU 资源的多实例 GPU（MIG）技术，以及随着部署从批处理向接近实时的床旁临床洞察转变，扩展至大量 NVIDIA Blackwell Ultra GPU 的能力。

'The opportunity for AI in healthcare is to give care teams more time and insight to focus on patients,' said Chelsea Sumner, Healthcare AI Startups Ecosystem Lead at NVIDIA. 'Dyania Health brings deep clinical expertise to that opportunity, using NVIDIA's AI platform to support research, help identify gaps in care and build a more complete understanding of patients' needs.'.

“人工智能在医疗保健领域的机遇在于，为护理团队提供更多时间和洞察，使其能够更专注于患者，”英伟达（NVIDIA）医疗保健人工智能初创企业生态系统负责人切尔西·萨姆纳（Chelsea Sumner）表示。“Dyania Health 将深厚的临床专业知识融入这一机遇，利用英伟达的人工智能平台支持研究、帮助识别护理中的不足，并构建对患者需求的更全面理解。”

'This is a watershed moment for patient care. Every patient's story is spread across years of notes, reports and results, and for the first time we can reason across all of it at once, the way a specialist does, for every patient in a health system rather than one chart at a time,' said Eirini Schlosser, founder and CEO of Dyania Health.

“这是患者护理领域的一个分水岭时刻。每位患者的病史分散在多年积累的笔记、报告和检查结果中，而我们现在首次能够像专科医生那样，对整个医疗系统中的每一位患者进行全面的综合分析，而非一次仅查阅一份病历。”Dyania Health 创始人兼首席执行官艾里尼·施洛瑟表示。

'Synapsis AI reads the full record, draws the clinical conclusions and shows the evidence behind them, and NVIDIA gives us the computing power to do that inside the hospital, with every record staying where it belongs. What we have shown with Cleveland Clinic is that a health system can learn from the patients it has already treated to give the next patient the right care sooner.'.

“Synapsis AI 读取完整病历，得出临床结论并展示其背后的证据，而 NVIDIA 为我们提供了在医院内部实现这一过程所需的算力，确保所有病历数据保留在应属的位置。我们与克利夫兰诊所的合作表明，医疗系统可以从已诊治的患者身上学习，从而为下一位患者更早地提供恰当的护理。”

Predicting how long replacement heart valves will last

预测人工心脏瓣膜的使用寿命

The first work from the collaboration is a Cleveland Clinic study of 7,812 patients who received an aortic valve replacement between 2005 and 2021. The study addressed a central concern for patients: How long will the replacement valve last, and will another procedure be needed? To answer this, researchers reviewed up to 16 years of patient care, most of it in narrative notes and echocardiogram reports that can be challenging to capture in registries..

这项合作的首项成果是克利夫兰诊所开展的一项研究，涉及2005年至2021年间接受主动脉瓣置换术的7,812名患者。该研究旨在解决患者普遍关心的一个核心问题：置换后的瓣膜能使用多久，是否需要再次手术？为回答这一问题，研究人员回顾了长达16年的患者诊疗记录，其中大部分信息以叙事性病历和超声心动图报告的形式存在，这些数据在注册数据库中往往难以完整捕获。

Running on NVIDIA accelerated computing inside Cleveland Clinic's environment, Synapsis AI identified the patient population and answered 88 clinical questions for each patient, covering the valve and its characteristics, co-existing conditions, laboratory trends, medications, follow-up ultrasound measurements and later clinical events.

在克利夫兰诊所的环境中，基于NVIDIA加速计算平台运行，Synapsis AI确定了患者群体，并为每位患者回答了88个临床问题，涵盖瓣膜及其特征、共存疾病、实验室检查趋势、用药情况、随访超声测量结果以及后续临床事件。

The study produced a detailed clinical dataset in far less time than a manual review would take. Additionally, a predictive machine-learning model was trained that is capable of predicting valve failure and reintervention risk timelines for every patient..

该研究在远少于人工审查所需的时间内，生成了一份详细的临床数据集。此外，还训练了一个预测性机器学习模型，能够预测每位患者的瓣膜衰竭及再次干预风险的时间线。

The model found that the type of valve implanted was the strongest determinant of how quickly it deteriorated, while age and other medical conditions shaped whether and when a second procedure was performed. The analysis also examined how follow-up information, such as heart ultrasounds following surgery, can substantially raise predictive accuracy around future interventions.

该模型发现，植入瓣膜的类型是决定其退化速度的最强因素，而年龄和其他健康状况则影响是否以及何时进行第二次手术。分析还考察了随访信息（如术后心脏超声检查）如何显著提高对未来干预措施的预测准确性。

The model estimates an individual patient's risk at three, five and 10 years and revises the outlook with each new ultrasound. The tool is used to help inform care but requires external validation from clinicians. .

该模型估算个体患者在三年、五年和十年时的风险，并随着每次新的超声检查结果更新预后。该工具用于辅助临床决策，但需要临床医生进行外部验证。

'Using technology to bring together and analyze information that can help us predict the duration of implanted heart valves is a promising step towards enhanced outcomes and more personalized care for patients,' said Lars G. Svensson, MD, PhD, Chief, Heart, Vascular & Thoracic Institute at Cleveland Clinic..

“利用技术整合并分析有助于预测植入式心脏瓣膜耐久性的信息，是迈向改善患者预后和提供更个性化护理的重要一步。”克利夫兰诊所心脏、血管与胸外科研究所主任拉斯·G·斯文森医学博士、哲学博士表示。

The heart valve study illustrates the broader potential of the collaboration. The same technology could help health systems learn from their own experience to understand which patients would benefit from certain therapies, which treatments hold up over time, and why outcomes differ between patients who look alike on paper.

心脏瓣膜研究彰显了此次合作的更广泛潜力。同一技术可帮助医疗系统从自身经验中学习，以了解哪些患者会从特定疗法中受益，哪些治疗方案在长期应用中依然有效，以及为何在纸面特征相似的患者之间，治疗结果却存在差异。

Looking ahead, Synapsis AI agents will continuously train predictive models of this kind, so that recommendations for the next recommended action in a patient's care keeps improving in accuracy as precision medicine advances..

展望未来，Synapsis AI 代理将持续训练此类预测模型，从而随着精准医疗的进步，不断提高患者诊疗中下一步推荐行动的准确性。

About Dyania Health

关于 Dyania Health

Dyania Health

戴亚尼亚健康

exists so that no patient is missed because the answer was buried in their chart. Its clinical reasoning engine, Synapsis AI, reads a patient's entire medical history, across years of notes, reports and results, and answers clinical questions with the evidence to back them, so care teams can see which patients need attention, which treatments are working and what should happen next.

其存在是为了确保不会因答案深埋于病历中而遗漏任何患者。其临床推理引擎 Synapsis AI 会读取患者多年来的笔记、报告和检查结果等完整病史，并提供有据可依的临床问题解答，从而使护理团队能够识别哪些患者需要关注、哪些治疗方案有效以及下一步应采取的措施。

It does this for every patient in a health system, inside the hospital firewall, with no record ever leaving. Health systems use Synapsis AI to find patients who need care they have not yet received, strengthen registries and quality programs, and turn their own clinical experience into better outcomes for the next patient.

它在医疗系统内、医院防火墙内部为每一位患者执行此操作，确保任何记录都不会外泄。医疗系统利用 Synapsis AI 来识别那些尚未接受所需护理的患者，加强患者登记和质量改进项目，并将自身的临床经验转化为改善下一位患者预后的能力。

Dyania Health is headquartered in Jersey City, New Jersey..

Dyania Health 总部位于新泽西州泽西市。

For more details, please visit Dyania Health's website at

如需了解更多详情，请访问 Dyania Health 的网站：

https://dyaniahealth.com/

https://dyaniahealth.com/

For Media Quiries: contact

媒体查询：联系方式

[email protected]

[email protected]

SOURCE Dyania Health

来源：Dyania Health