Enterprise Imaging and AI –  What Does the Future Hold?

The opportunity and potential of Artificial Intelligence (AI) in Enterprise Imaging go well beyond pixels. AI can optimize and automate several monotonous tasks within Enterprise Imaging workflows, streamlining processes and assisting doctors in providing more accurate, efficient, and personalized treatment options for their patients. With the help of AI’s deep learning algorithms, enterprise imaging technology now enables medical practitioners to identify abnormalities and detect diseases with a higher level of precision and superior speed than ever before.

AI could also evolve into identifying protocols for the most commonly occurring clinical indications with minimal protocol variability, allowing radiologists to focus on more complex cases. This has contributed to significant improvements in the accuracy of diagnosis, the efficiency of treatment, and the overall quality of patient care.

In this blog, over the coming year, we will explore the benefits of PaxeraHealth’s AI-assisted imaging suite and how it is helping to transform healthcare. We will discuss the principal steps of using AI algorithms to improve your radiological operational efficiencies and workflow management, with the intention of providing a broader understanding of the value of applying AI across Enterprise Imaging departments. Additionally, we’ll share relevant industry insights along the way. Let’s dive in with a view of the tech landscape and a sense of where AI fits in today and where it’s heading tomorrow.

Healthcare organizations leverage a wide variety of Enterprise Imaging tools to assist in diagnosis, care delivery, and disease management. There are many configurations with imaging modalities, PACS, VNA, Image Exchange, and specialty reporting/viewing systems deployed to capture, store, route, and effectively present images to clinicians and patients. One relatively dominant model for AI deployment in this image value chain is to make AI diagnostic assistance services available via cloud interaction before viewing in the PACS. This model requires providers to send exact copies of digital images (and other EHR data) to the AI cloud infrastructure and receive feedback/guidance in either a few minutes or, in some cases with bulk transmission, a few hours. When deployed in this fashion, there is inherent privacy risk based on the transmission of the images/data, and there is a significant workflow challenge in requiring long processing times before getting results. Imagine performing a CT for a patient in the hospital and waiting four hours for any help in diagnosis from an AI tool in the cloud. Not exactly mission-critical response time!

In addition, many of the cloud AI offerings come with their own set of viewers and reporting tools. This allows for faster use in the field, but it comes with a significant workflow price — clinicians have to leave their standard environment to work with a new tool in a new UI. Moreover, if an AI deliverable can assist in one type of Imaging study (e.g., chest X-ray) it’s likely that the next AI tool (e.g., PET scan) will be in its own, entirely separate, and entirely new environment. Multiply this by the amount of expected AI, and this arrangement will negatively impact clinician productivity.

Another phenomenon in the imaging space is a focus on AI for diagnostic assistance. While that focus is useful, there are less developed but valuable opportunities to develop AI tools focused on productivity. Worklist assignment, patient prioritization, staff load balancing, schedule optimization, appointment management, and even revenue cycle processes can also benefit from the smart application of AI to perform tasks and find better ways to improve the patient experience.

Finally, AI development today is focused on academic environments and/or start-up labs that often lack the kind of real-world data that matches their intended markets. Developers are doing amazing work, and we’ve seen a set of initial models that can make a substantial difference. If we maintain the current pace of AI development, we will be waiting too long to get these necessary innovations to market. The key barrier to development is that the coding background required to build AI is highly specialized and highly compensated, creating a supply-side challenge. We need more development, and we can’t scale current tools and talent to get us there.

With the defined challenges of getting to VALUE with AI in Enterprise Imaging, it seems a bit daunting. Should we simply give up and declare AI an interesting toy? Or, is a new model required for AI authoring, consumption, storage, and real use that can actually work?

PaxeraHealth is committed to delivering the leading technology solutions that embed AI in all elements of the Enterprise Imaging continuum to help improve productivity, quality, and patient care. Our approach is a “foundation first” model where we enable AI to be developed and run inside the enterprise imaging suite. Our tagline and focus of the company – Elevating Healthcare through AI Assisted Imaging – underlies our solution suite: No-code tools and embedded workflow to drive performance are the future of AI and the future of imaging.

Watch this space for more….