Explore Your Local Site

Looks like you've landed on our   site. Let's take you home:    

Please note that the content and products on the    site might not be available in your region.

 

Choose the language:

  Homepage
Continue on the current website:  

 

Bayer and Rad AI Announce Collaboration Agreement

 

Collaboration brings together Rad AI’s generative AI radiology reporting solutions and patient follow-up management solution with Bayer’s Calantic™ Digital Solutions Platform

 

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.

 

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.

 

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.

 

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

 

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

 

"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.

 

“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. 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.

 

About Bayer

 

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

 

About 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.

 

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.

 

Forward-Looking Statements

 

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. 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.

 

1S. 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.

 

2024-06-27

 

 

Share

Spark Report

RELATED RESOURCES

eBook

 

Automated Follow-Up – The within health Approach

Adam Kirell, CEO of ‘Within Health’®, explained how his company facilitates an important part of patient management in radiology: His application tries to make sure that patients get their clinically recommended follow-up.

 

Currently, approximately 50% of all patients in the US miss out on their follow-up exams. This not only delays diagnosis, but a missed follow-up exam might contribute to litigation risk. Part of the problem are oftentimes manual and fragmented back-office workflows in these institutions.

 

Kirell’s system automates these processes by:

  • Finding patients lost on follow-up by browsing radiological reports with natural language processing
  • Communicating with these patients and the care-team
  • Directing these patients to the scheduling team
  • Following-up on the appointment and
  • Making sure the case can be closed after exams have been performed OR escalating the case

 

This automated process has highly increased the efficiency of output coordinators, i.e. the staff organizing radiology back-offices: “One output coordinator can now handle almost three times as many patients as before”, said Kirell. This result is particularly important, as many institutions suffer from a lack of staff.

 

In order to generate more benefit for the radiology ecosystem, his application needs to be integrated smoothly with other applications – which is a key factor for him to be part of CalanticTM SPARK. With Calantic SPARKTM, he expects to knock down barriers like the integration with other applications or technical bandwidth.

 

Leveraging Imaging Data – The VIDA Approach

Todd Johnson, CTO of VIDA®, talked about AI from a more disease-oriented angle - VIDA® processes chest CT images. The company develops AI-derived imaging biomarkers, i.e. markers for lung diseases extracted from different imaging metrics. These biomarkers may support physicians – pulmonologists or radiologists – in order to take a diagnostic or treatment decision.

 

One of Johnson’s examples was the use of one biomarker as a predictor for the success of an endobronchial lung resection procedure. This biomarker is now already used in the daily clinical practice at the University of Wisconsin. Other biomarkers are already used to predict interstitial lung disease.

 

The huge amount of collected imaging data includes vast numbers of biomarkers per clinical time point – what Johnson calls an “imaging biobank”. AI now uses this data to look at the progression of different diseases and model diseases.

 

The data is also used to optimize and accelerate clinical trials: the technology can be used to create e.g. a synthetic control arm, like “digital twin”. In the end, the biobank reduces the number of subjects required for a new clinical trial.

 

Regarding Calantic® SPARK, Johnson sees two major benefits of this global and curated platform: He believes that with a partner like Bayer, new applications might gain easier access to the global market. In addition, there might be a chance to build relationships with key academic facilities worldwide.

 

 

Presentation Title: Collaboration Sparks Innovation: How Bayer is Engaging with the Startup Ecosystem: Bayer Digital Solutions

Source: RSNA 2022

Author: kf/ktg

Last update: 1st February 2023