KLAS Report Finds AI Pervading Imaging, Says Executives Looking at Strategies That Leverage Tools Without Significantly Increasing Complexity

The field of AI is rapidly transforming healthcare, offering new opportunities to enhance diagnostic accuracy, operational efficiency, and patient outcomes. According to the KLAS report, “Imaging AI 2024: Multiple Solutions Gaining Traction in a Crowded Market,” the adoption of imaging AI has accelerated significantly over the past five years. More than 50% of surveyed organizations are now actively using AI algorithms for at least one imaging use case. That is an increase from just 17% in 2018. For health system IT executives, this momentum presents both opportunities and challenges as they navigate a market with over 300 FDA-approved solutions and a growing number of vendors.

A Growing Trend: The Rise of Imaging AI

The report states that imaging AI is no longer a niche experiment confined to a few early adopters. Its use is expanding across various clinical and operational applications, with health systems of all sizes exploring its potential. Neurology and stroke imaging have emerged as dominant use cases, overtaking long-established computer-aided detection (CAD) tools in breast imaging. Other key applications include pulmonary embolism detection, automated impression generation for radiology reports, and accelerated MRI imaging.

Not surprisingly, organizations conducting more than 500,000 imaging studies annually are leading the charge in AI adoption. These high-volume health systems often have the resources and patient throughput to realize the benefits of AI more quickly. However, the report highlights that mid-sized organizations, conducting between 100,000 and 499,000 studies annually, are also rapidly catching up. Many of these organizations are actively planning their first AI implementations.

A Crowded and Evolving Vendor Landscape

The competitive landscape for imaging AI solutions is increasingly diverse. There are dozens of vendors offering platforms and algorithms tailored to specific clinical needs. According to the report, RapidAI and Viz.ai have the highest adoption rates among surveyed organizations. RapidAI is particularly valued for its neurovascular applications, including stroke detection and perfusion color mapping. Viz.ai is similarly focused on stroke care but is also gaining traction in cardiology applications.

Aidoc stands out as the most-considered vendor in the report, thanks to what users consider is its versatile AI platform. As one respondent explained, “Aidoc is like a platform. We pay a certain amount per case and get all the algorithms. The vendor is always expanding the platform.” This flexibility appeals to health systems that require a range of AI tools but want to avoid the complexity of managing multiple vendors.

Platforms like Nuance’s Precision Imaging Network (PIN) are also gaining attention for their ability to centralize multiple AI solutions within a single interface. According to the report, platforms are increasingly seen as “marketplaces” that offer scalability and simplified management. This trend aligns with the broader industry shift toward integrated solutions that can seamlessly plug into existing workflows.

Emerging Use Cases and Innovations

Beyond traditional diagnostic applications, imaging AI is finding new roles in workflow optimization and operational efficiency. AI tools are being used to triage worklists, prioritize critical cases, and automate routine reporting tasks. For example, Rad AI’s solution for impression generation pre-populates radiology reports with AI-generated interpretations based on years of radiologist dictations.

Other emerging use cases include accelerated image acquisition, such as AIRS Medical’s SwiftMR solution for MRI, and automated lung nodule detection with tools like Riverain Technologies’ ClearRead CT.

Challenges on the Path to Adoption

Despite its promise, the report highlights several barriers to wider imaging AI adoption. Cost remains a significant hurdle, particularly for mid-sized and smaller health systems. One respondent shared, “Trying to get anybody to spend any money right now is very tough,” reflecting the financial constraints many organizations face in today’s healthcare environment.

Integration challenges are another common concern. Health systems often hesitate to adopt new AI tools that may not seamlessly integrate with their existing PACS or other imaging infrastructure. To address this issue, traditional imaging vendors like Siemens Healthineers and GE HealthCare are introducing AI-enhanced capabilities that integrate directly into their established platforms. These solutions allow health systems to leverage AI without the need for significant workflow disruptions or additional IT complexity.

Another challenge is the crowded vendor landscape itself. With so many options available, IT executives must carefully evaluate the capabilities, costs, and interoperability of different solutions to ensure they align with their organization’s clinical and operational goals.

Actionable Takeaways for Health System Leaders

For IT executives looking to navigate the complex imaging AI market, the following strategies can help maximize the value of their investments:

• Prioritize High-Impact Use Cases: Focus on applications that address the most pressing clinical or operational needs, such as stroke care, pulmonary embolism detection, or automated reporting.
• Explore Platform Solutions: Opt for AI platforms that consolidate multiple algorithms, reducing the complexity of managing separate tools from different vendors.
• Engage Established Vendors: Work with traditional imaging vendors that offer AI-enhanced solutions integrated into existing PACS and other systems, minimizing disruption.
• Assess Scalability: Choose solutions that can grow with your organization, allowing you to add new use cases or algorithms as needs evolve.
• Monitor Industry Trends: Stay informed about new FDA approvals, emerging vendors, and evolving AI applications to remain competitive and forward-thinking.

A Vision for the Future

The KLAS report paints a compelling picture of the future of imaging AI. As adoption continues to grow, the technology is expected to become an integral part of radiology and diagnostic workflows across health systems. Vendors that fail to develop robust AI strategies risk being left behind in a market increasingly defined by innovation and integration.

The report underscores the importance of embracing AI in imaging: “There really is no future where AI does not play an important role in imaging IT solutions, and therefore, vendors must have a defined AI strategy or risk being left behind by more AI-friendly vendors.”

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