Written by Alexandre Salvador | Head of Digital Solutions Business, Bayer Radiology, Berlin, Germany
Disclaimer: This content was commissioned and funded by Bayer AG.
The Frontrunner of Digital Innovation
The healthcare industry is undergoing a major overhaul in terms of technology, and artificial intelligence (AI) in particular has huge potential to enhance all areas of patient care, from research and development of new therapy options through to diagnosis and treatment of individual patients.
The breakthrough in AI is the result of advances in data collection and aggregation, processing power, and deep learning algorithms. Some of the most promising AI applications in healthcare have been in image processing and image analysis, which encompasses the remit of radiology.
The medical imaging field has long been a frontrunner of digital innovation in healthcare and now also in the application of AI. We are seeing the adoption of AI tools to reduce labour-intensive and repetitive tasks, such as the analysis of medical images.1 AI has also been adopted in other areas, including medical image denoising to enable the reduction of scan time, dose reduction, automatic segmentation of anatomical and pathological features such as in oncology imaging, and image reconstruction.1 Most recently, we have also seen an increase in the use of AI techniques in imaging data acquisition, segmentation, and diagnosis for patients with coronavirus disease (COVID-19), all of which are intended to help medical specialists.2
A Challenging Environment
The rise of AI comes at the right time for radiology teams, and not only because of the COVID-19 pandemic. The perfect storm of a globally ageing population and changing lifestyles is leading to an exponentially growing need for medical imaging to facilitate diagnosis, treatment decisions, and therapy planning. At the same time, medical imaging data continue to multiply and have become ever more complex at a disproportionate rate when compared with the number of available trained readers. As a result, the workload of radiology teams has grown dramatically, with few signs of slowing down. In some cases, just seconds are available to interpret each medical image, increasing the risk of diagnostic errors and inefficiencies in therapeutic decision-making.3
Consequently, there is a mounting demand for disruptive technologies and integrated solutions that improve the efficiency of workflows used within radiology departments. However, a key issue is that current solutions are scattered and largely address isolated problems. To leverage the full spectrum of options available today, radiology suites must host several individual applications. This means that radiologists must switch between systems and platforms to alternate between applications, eating into already limited time and resources.
Bayer and Blackford
To address the issue, Bayer has entered into a strategic agreement with Blackford, a company that offers a platform technology for the effective selection, deployment, and management of multiple medical imaging applications and AI. Together, we will establish a single platform providing access to a curated marketplace, through which healthcare professionals can centrally manage digital clinical imaging and workflow applications, including AI-enabled solutions, created by Bayer and strategic partners. Bayer will develop and compile these solutions with disease management in mind. Integrated into the medical imaging workflow, these offerings will aim to support the complex decision-making processes of radiologists and their teams.
As a life science company with a long history in diagnostics and therapeutic innovations, we are in the unique position to assist healthcare professionals in making informed decisions at critical steps within a patient’s treatment journey. Our agreement with Blackford reinforces our commitment to innovation with overall disease management in mind; by driving AI-enabled medical imaging, we want to support radiologists and their teams in providing clear direction from diagnosis to care. The goal will be to deliver solutions that address the issues they face so that, ultimately, they can spend more time where it really matters: with patients.
To learn more about Bayer’s radiology products and services, please visit our website: https://www.radiology.bayer.com
For UK audiences, please visit www.radiology.bayer.co.uk
References
- Lewis SJ et al. Artificial Intelligence in medical imaging practice: looking to the future. J Med Radiat Sci.2019;66(4):292-5.
- Shi F et al. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Rev Biomed Eng. 2020;DOI:10.1109/RBME.2020.2987975. [Epub ahead ofprint].
- Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-510. doi:10.1038/s41568-018-0016-5
PP-PF-RAD-GB-0270 / December 2020
