NATIONAL HARBOR, Md.--(BUSINESS WIRE)--As experts from around the globe gather at the annual Society for Informatics in Medicine (SIIM) conference, Bayer and Rad AI are announcing a collaboration to bring Rad AI’s cutting edge AI radiology operational solutions to Calantic ™ Digital Solution customers..

马里兰州NATIONAL HARBOR（BUSINESS WIRE）--随着来自全球的专家聚集在医学信息学会（SIIM）的年度会议上，拜耳和Rad AI宣布合作，将Rad AI的尖端AI放射学操作解决方案带给Calantic™数字解决方案客户。。

Rad AI’s radiology speech recognition reporting solution, AI-driven patient follow-up management, and automated radiology impression generation technologies complement Bayer’s Calantic™ Digital Solutions platform, enabling more hospitals and health systems to benefit from Rad AI’s generative AI capabilities..

Rad AI的放射语音识别报告解决方案、人工智能驱动的患者随访管理和自动放射印象生成技术补充了拜耳的Calantic™数字解决方案平台，使更多的医院和卫生系统能够受益于Rad AI的生成人工智能功能。。

Imaging data accounts for about 90 percent of all healthcare data1, and the number of images continues to grow, increasing the workload for radiologists on top of resource constraints. At the same time there is a growing need to demonstrate value from AI deployments. This collaboration enables Radiology suites to take advantage of operational Radiology AI applications and a scalable deployment platform integrated through one vendor..

成像数据约占所有医疗保健数据的90%1，图像数量持续增长，除了资源限制外，还增加了放射科医生的工作量。与此同时，越来越需要证明人工智能部署的价值。这种协作使Radiology Suite能够利用可操作的Radiology AI应用程序和通过一个供应商集成的可扩展部署平台。。

Rad AI Reporting helps radiologists create reports faster by using generative AI to organize radiology reports for radiologists allowing them to focus on reading studies as well as integrating stable findings from prior reports. Rad AI Omni Impressions generates customized radiology report impressions from dictated findings, tailored to each radiologist’s language preferences and helping to reducing fatigue.

Rad AI Reporting通过使用生成性AI为放射科医生组织放射学报告，帮助放射科医生更快地创建报告，使他们能够专注于阅读研究以及整合先前报告中的稳定发现。Rad AI Omni Impressions根据口述的发现生成定制的放射学报告印象，并根据每位放射科医生的语言偏好进行定制，有助于减少疲劳。

Rad AI Continuity automates the follow-up process for incidental findings, helping to promote timely patient follow-up, and potentially increasing imaging revenue for health systems..

Rad AI连续性自动化了偶然发现的后续过程，有助于促进及时的患者随访，并可能增加卫生系统的成像收入。。

Calantic™ Digital Solutions is a suite of digital radiology AI-enabled clinical and operational applications that assist radiologists and their teams at critical steps within a patient’s journey through the Radiology suite. The Calantic platform is a vendor-neutral, cloud-hosted platform which includes a growing number of applications designed to aid in prioritization, lesion detection and quantification, as well as apps that automate routine tasks, measurements, and improve workflows in Radiology suites.

。Calantic平台是一个供应商中立的云托管平台，其中包括越来越多的应用程序，旨在帮助确定优先级，病变检测和量化，以及自动化常规任务，测量和改进放射套件工作流程的应用程序。

The platform uses a curation approach to select apps based on structured assessment criteria..

该平台使用策展方法根据结构化评估标准选择应用程序。。

'Rad AI and Bayer are dedicated to pioneering innovations that serve hospitals and health systems, allowing for greater access to these advanced technologies. This relationship allows for expanded use of our cutting-edge solutions in hospitals across the country,' Doktor Gurson, co-founder and CEO of Rad AI..

“Rad AI和拜耳致力于为医院和卫生系统服务的开创性创新，从而可以更好地获得这些先进技术。Rad AI联合创始人兼首席执行官Doktor Gurson说，这种关系允许我们在全国各地的医院扩大使用我们的尖端解决方案。。

“Our customers consistently convey to us that a clear ROI from the use of AI will help to increase confidence and adoption. Demonstrating ROI via operational applications like those on Bayer’s Calantic™ Digital Solutions platform is often an easier path, which is why Bayer is excited to enter into this agreement with Rad AI.

“我们的客户始终向我们传达这样一个信息，即使用人工智能产生的明确投资回报率将有助于提高信心和采用率。通过拜耳Calantic™数字解决方案平台等运营应用程序展示投资回报率通常是一条更容易的道路，这就是拜耳很高兴与Rad AI签订此协议的原因。

This technology has the ability to help physicians optimize and streamline their radiology reporting, and deliver benefits for the patient and the health system,” said Rich Dewit, Senior Vice-President, Digital Solutions, Bayer Radiology..

这项技术能够帮助医生优化和简化放射学报告，并为患者和卫生系统带来好处，”拜耳放射学数字解决方案高级副总裁Rich Dewit说。。

About Bayer

拜耳简介

Bayer is a global enterprise with core competencies in the life science fields of health care and nutrition. Its products and services are designed to help people and the planet thrive by supporting efforts to master the major challenges presented by a growing and aging global population. Bayer is committed to driving sustainable development and generating a positive impact with its businesses.

拜耳是一家全球性企业，在医疗保健和营养等生命科学领域拥有核心竞争力。它的产品和服务旨在通过支持应对全球人口不断增长和老龄化所带来的重大挑战，帮助人类和地球繁荣发展。拜耳致力于推动可持续发展，并对其业务产生积极影响。

At the same time, Bayer aims to increase its earning power and create value through innovation and growth. The Bayer brand stands for trust, reliability, and quality throughout the world. In fiscal 2022, the Group employed around 99,723 people and had sales of 50.7 billion euros. R&D expenses before special items amounted to 6.2 billion euros.

与此同时，拜耳的目标是通过创新和增长来提高其盈利能力和创造价值。拜耳品牌代表着全世界的信任、可靠性和质量。。特殊项目前的研发费用为62亿欧元。

For more information, go to www.bayer.com..

欲了解更多信息，请访问www.bayer.com。。

About Rad AI

关于Rad AI

Rad AI is a radiologist-led AI company. The company has been recognized as one of the most promising healthcare AI companies by CB Insights (Digital Health 50, AI 100) and AuntMinnie (Best New Radiology Software of 2023, Best New Radiology Vendor of 2021). In Dec 2023, an Accenture report noted that Rad AI has the highest influence score and brand buzz of any private company in radiology..

。该公司被CB Insights（Digital Health 50，AI 100）和Auntminie（2023年最佳新放射软件，2021年最佳新放射供应商）评为最有前途的医疗保健AI公司之一。2023年12月，埃森哲（Accenture）的一份报告指出，Rad AI在放射学领域拥有最高的影响力得分和品牌口碑。。

Founded by the youngest US radiologist in history, Rad AI empowers physicians with Al that can potentially save time, reduce burnout, and improve the quality of patient care. Rad AI combines deep expertise in healthcare and AI while building on voluminous proprietary radiology report datasets.

Rad AI由美国历史上最年轻的放射科医生创立，为医生提供Al，可以节省时间，减少职业倦怠，提高患者护理质量。Rad AI结合了医疗保健和人工智能领域的丰富专业知识，同时基于大量专有的放射学报告数据集。

Forward-Looking Statements

前瞻性声明

This release may contain forward-looking statements based on current assumptions and forecasts made by Bayer management. Various known and unknown risks, uncertainties and other factors could lead to material differences between the actual future results, financial situation, development or performance of the company and the estimates given here.

本版本可能包含基于拜耳管理层当前假设和预测的前瞻性声明。各种已知和未知的风险、不确定性和其他因素可能导致公司的实际未来业绩、财务状况、发展或业绩与此处给出的估计之间存在重大差异。

These factors include those discussed in Bayer’s public reports which are available on the Bayer website at www.bayer.com. The company assumes no liability whatsoever to update these forward-looking statements or to conform them to future events or developments..

这些因素包括拜耳网站www.Bayer.com上拜耳公开报告中讨论的因素。公司不承担更新这些前瞻性声明或使其符合未来事件或发展的任何责任。。

1, S. K. Zhou et al., 'A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises,' in Proceedings of the IEEE, vol. 109, no. 5, pp. 820-838, May 2021, doi: 10.1109/JPROC.2021.3054390.

1，S.K.Zhou等人，《医学成像深度学习综述：成像特征，技术趋势，具有进展亮点的案例研究和未来承诺》，载于《IEEE学报》，第109卷，第5期，第820-8382021年5月，doi:10.1109/JPROC.2021.3054390。