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Check out the new eBook: Artificial Intelligence in Breast Cancer Imaging Download now

Radiologists are facing barriers hindering AI adoption in clinical practice

Workflow integration

Workflow integration

67% radiologists highlight workflow mapping and implementation as a barrier.1

Lack of trust

Lack of trust

Only 36% of 100 CE marked AI applications or medical solutions supported by evidence in peer-reviewed publications based on one analysis.2

Rise of AI applications

Rise of AI applications

Over 700 FDA-cleared AI radiology apps in the US and more than 200 with CE certification in the EU.3,4

Rise of AI applications

Rise of AI applications

Over 700 FDA-cleared AI radiology apps in the US and more than 200 with CE certification in the EU.3,4

Introducing

Calantic – the AI Radiology Solution from Bayer

Calantic gives radiologists access to tools and AI applications, that support worklist prioritization, detection, and quantification – with intuitive functionality tailored for specific disease areas.

 
 
 
 
  • Edge

    Enables automatic routing of pseudonymized studies from the PACS, RIS and/or EMR to the right Al application.

  • Viewer

    Smoothly accessible through the PACS, provides a unified user interface for interacting with Al findings. Al results can be approved or rejected by the radiologist.

  • Curated 
    Marketplace

    Provides a curated selection of AI applications bundled in specialty-driven clinical service lines.

  • Analytics

    Offers insights into real -world Al performance in your practice. Benchmark the clinical success of Al implementation and continuously monitor.

Cloud-housted platform

Cloud-hosted platform that provides access 
to AI applications

Access to all installed applications through 
a single common user interface

Access to all installed applications through a single common user interface

Zero-footprint CalanticTM Viewer integrated into 
the PACS viewport

Zero-footprint Calantic Viewer smoothly accessible through the PACS

Our Clinical Service at a Glance

Dive into our tailored solutions thoughtfully categorized into service lines spanning from thoracic to neurology, cardiovascular to breast, and more.

Our Clinical Service at a Glance
Our Clinical Service at a Glance

Why Radiologists Trust Calantic

Dr. Arsany Hakim

In a busy radiology workflow, relying on an efficient, fully automated platform is crucial to leverage the clinical advantages offered by AI tools, such as triaging, assisted detection, and advanced imaging biomarkers integral to modern neuroradiology. With the growing number of AI applications, having an interoperable solution that provides a unified interface, streamlined monitoring, and centralised support undoubtedly saves time.
Calantic accelerates the diagnostic process with the operational efficiency necessary to enhance patient care.”

Dr. Arsany Hakim
Neuroradiologist at Inselspital Bern

Dr. Bruno De Peuter

By adopting the Calantic AI platform at Ziekenhuis Oost-Limburg, we aim to enhance our diagnostic efficiency. We are confident this technology has the potential to empower our radiology team and, ultimately, serve our patients better.”

Dr. Bruno De Peuter
Head of Medical Imaging at Zeikenhuis Oost-Limburg

Dr. Jorge Salmerón

From the radiological-clinical perspective, at Instituts Guirado we are convinced that with the Calantic platform, there will be a before and after for lung nodules diagnosis and follow-up.”

Dr. Jorge Salmerón
Medical Director at Instituts Guirado

Dr. Jorge Salmerón

Delighted that Calantic joins us in our effort to provide the best possible state of the art imaging to our patients.”

Dr. Filip Van Grimberge
Head of Department Radiology AZ Turnhout

Explore the benefits of Artificial Intelligence in Radiology
Explore the benefits of Artificial Intelligence in Radiology

How can AI potentially address the challenges faced by radiology departments today? Get insights into promising clinical uses of artificial intelligence in Radiology.

Read Online
Discover the future of Cancer Screening with AI
Discover the future of Cancer Screening with AI

How can the use of AI in radiology potentially improve cancer screening and detection? Examine the research about the use of AI in breast, colorectal, hepatocellular, and prostate cancer screening.

Read Online
Gain Insight into AI's Impact on Neurology
Gain Insight into AI's Impact on Neuroradiology

This eBook outlines the most common applications of AI in neuroradiology and discusses the evidence behind them.

Read Online

References 

1.
Adapted from Strohm L, Hehakaya C, Ranschaert ER, Boon WPC, Moors EHM. Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. Eur Radiol. 2020 Oct;30(10):5525-5532. doi: 10.1007/s00330-020-06946-y.
2.
Van Leeuwen KG, Schalekamp S, Rutten MJCM, van Ginneken B, de Rooij M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol. 2021 Jun;31(6):3797-3804. doi: 10.1007/s00330-021-07892-z. Epub 2021 Apr 15. PMID: 33856519; PMCID: PMC8128724.
3.
Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices. U.S. Food & Drug Administration. Updated August 7 2024. Accessed August 23, 2024. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices
4.
Available online: https://grand-challenge.org/aiforradiology/ [Accessed 23/08/2024]
 
 
 
 
  • Edge

    Enables automatic routing of pseudonymized studies from the PACS, RIS and/or EMR to the right Al application.

  • Viewer

    Smoothly accessible through the PACS, provides a unified user interface for interacting with Al findings. Al results can be approved or rejected by the radiologist.

  • Curated 
    Marketplace

    Provides a curated selection of AI applications bundled in specialty-driven clinical service lines.

  • Analytics

    Offers insights into real -world Al performance in your practice. Benchmark the clinical success of Al implementation and continuously monitor.

Accordion header

    1.
    McDonald, RJ, Schwartz, K, Eckel, L. et al.,(2015), The Effects of Changes in Utilization and Technological Advancements of Cross-Sectional Imaging on Radiologist Workload, Academic Radiology, 22(9), 1191-1198
    2.
    Kane, L. (2022) Medscape National Physician Burnout & Suicide Report 2022: The Generational Divide. Medscape Retrieved August 17, 2022 from https://www.medscape.com/slideshow/2022-lifestyle-burnout-6014664?reg=1#2
    3.
    Itri, J. N., Tappouni, R. R., McEachern, R. O., et.al. (2018) Fundamentals of Diagnostic Error in Imaging. RadioGraphics 38, 1845-1865. Retrieved August 17, 2022 from  https://pubs.rsna.org/doi/epdf/10.1148/rg.2018180021