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Frost & Sullivan's white paper highlights the process of creating an AI strategy for your imaging department
SANTA CLARA, Calif., April 6, 2021 /PRNewswire/ -- The medical imaging industry is in the midst of large-scale transformation, largely driven by the advancements in technologies such as artificial intelligence (AI) and analytics, digitalization of radiology, as well as the rise of precision medicine and teleradiology. These cutting-edge technologies are aimed at addressing challenges in clinical, operational, and staff and patient experiences. In addition, they will resolve critical business pain points such as reimbursement cuts, sub-optimal use of imaging, radiologist burnout, missed appointments, and shortage of radiology subspecialists.
Frost & Sullivan's latest white paper, A practical Guide for the Implementation of Artificial Intelligence in Medical Imaging, highlights the major challenges in imaging and explores how AI can be leveraged to solve these challenges. It also covers the likely pitfalls during the implementation process and some of the key factors to consider for the successful implementation of an AI program. Furthermore, it reveals how this pandemic presents an opportunity for technology companies to prove their claims by demonstrating a positive return on investment (ROI) from clinical, operational, and financial perspectives.
To download the complimentary white paper, please visit: http://frost.ly/5hj
"A standards-based and interoperable enterprise imaging platform forms the robust framework needed to integrate AI algorithms within the clinical workflow with ease. Implementation of AI algorithms in siloes will lead to the scattered and isolated realization of intended benefits by the provider organizations," observed Dr. Suresh Kuppuswamy, Healthcare & Life Sciences Industry Principal at Frost & Sullivan. "To have a successful implantation of an AI program, hospitals should focus on aligning with a technology partner who can relieve them of the arduous tasks of exploring, validating and integrating various algorithms for a specific clinical application."
"Another important consideration is the seamless integration of the AI program into the existing radiology workflow. With RUBEE™ for AI, our approach of building an ecosystem of augmented intelligence powered by machine learning and workflow automation engine is unique, logical, and builds the foundation for our client's future growth," noted Dr. Anjum Ahmed, Global Chief Medical Officer at Agfa HealthCare. "By providing diagnosticians with carefully curated AI decision support tools, embedded in their Enterprise Imaging solution, we support them to maximize the value of their own skills and expertise, and become consultative powerhouse of evidence-based intelligence."
To achieve optimum results, hospitals will need to:
- Seek out a partner that demonstrates a high level of expertise in understanding the problem, is laser-focused in their approach to solving the problem by detailing the steps at each stage and customizing the solution to the organization's specific needs.
- Understand that AI implementation should fit into their long-term growth strategy and consider the flexibility of designing or redesigning their own proprietary AI validation models based on their specific data needs and the evolution of their organizations.
- Be wary of vendors that promise a fully automated solution as the instances of regulatory approvals of such algorithms are extremely rare. Evaluate AI vendors based on the quantum of data, type of setting their algorithm was trained on, and the intended use approved by the regulatory agency.
About Frost & Sullivan
For six decades, Frost & Sullivan has been world-renowned for its role in helping investors, corporate leaders and governments navigate economic changes and identify disruptive technologies, Mega Trends, new business models and companies to action, resulting in a continuous flow of growth opportunities to drive future success.
Contact:
Mariana Fernandez
Corporate Communications
E: mariana.fernandez@frost.com
http://ww2.frost.com
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