AI Improves Imaging Efficiency and Enables Integrated Care
Many AI algorithms are already available to support diagnosis, make image interpretation more efficient, augment clinical decision-making, inform procedural interventions and therapies, and even support utilization management and authorizations.
However, the power of AI in diagnostic imaging is frankly underused. The reason for this underutilization is that many of these AI models work in silos: They’re not integrated into the radiology workflow in ways that make them usable or useful.
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But that’s changing. Today, integrated workflow networks transform how radiologists and other imaging stakeholders can use AI to improve clinical and financial outcomes as well as the radiology experience.
What’s particularly valuable about an integrated workflow network is not the AI models that support earlier diagnosis, nor is it the enhanced collaboration and sharing that can happen seamlessly between providers and patients. Rather, the true value in these networks is the real, end-to-end patient care solutions that are emerging.
For example, lung cancer screening programs are now exceedingly common, and for good reason. In fact, we know that there is a compelling case for better lung health all around, and chronic lung conditions — in addition to cancer — are underdiagnosed. Early diagnosis of these conditions is essential, and the emergence of lung health programs contributes to better outcomes and improved quality of life for patients, not to mention the financial returns for healthcare organizations.