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Andrew MacLean

4 Critical Insights on AI’s Impact in Spinal Fracture Care: A Deep Dive Into Clinical Studies

C-Spine Fractures, Patient Management, Triage, Vertebral Compression Fracture, Workflow
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AI is changing the way physicians identify and treat spinal fractures. As AI becomes increasingly integrated into medical workflows, it’s essential for us to assess its performance and the implications it may have in clinical settings. Two recent studies–one focused on cervical spine fractures and another on vertebral compression fractures (VCFs)–highlight how AI is becoming a game changer for both radiologists and patients alike.

Here are some insights we came away with when looking at the data and the broader implications on AI and its role in treating patients with either of these pathologies:

1. AI Helps Prioritize Cases, Potentially Improving Care

When radiologists are faced with multiple imaging studies, tools that help prioritize urgent cases can make a significant impact. In the cervical spine study published in the European Journal of Radiology, AI algorithms flagged suspected cervical spine fractures on CT scans, helping clinicians prioritize these cases. In prospective cases where AI was used, the time between scan acquisition and when a radiologist first opened the scan was reduced by 16 minutes. 

This prioritization can help radiologists focus on the most critical cases sooner, potentially leading to faster interventions when necessary. Even a few minutes of time saved can make a world of difference in patient outcomes, especially in cases involving spinal injuries that require urgent attention.

2. Underreporting of Fractures Remains a Problem

One of the key findings from the VCF study was the high rate of underreported fractures. The study revealed that only 30% of vertebral compression fractures were identified by the radiologists. Many fractures went unflagged, highlighting the potential value of AI in assisting radiologists by flagging cases that may otherwise be overlooked.

By flagging potential fractures, AI can prompt a second look, ensuring that fewer patients are lost to follow-up. This is especially important given the link between VCFs and future osteoporotic fractures. While AI can’t guarantee that patients will receive treatment, increasing the visibility of suspected fractures is a step toward improving follow-up care.

3. AI Streamlines Workflow–but Broader System Integration is Key to Realizing Full Benefits

While AI tools can improve workflow efficiency by helping radiologists prioritize suspected fractures, the full benefits depend on how these tools are integrated into the overall clinical process. The cervical spine study highlighted that AI reduced the time to flag potential fractures, but this didn’t always translate into faster reporting. That’s because radiologists followed local protocols that required immediate communication with the treating physician once a fracture was flagged, irrespective of the time it took to finalize the radiology report. 

For AI to realize its full potential, it’s essential to ensure that the technology is well-integrated into the broader clinical workflow. This means aligning AI’s ability to flag cases with downstream processes like timely communication and patient management outside the radiology department.

4. AI is Set to Enhance Standard Clinical Practice, but There’s More Work Ahead

While AI is already proving itself as a useful tool in identifying potential spinal fractures, the studies show that there’s still work to be done. In the VCF study, the algorithm’s ability to flag fractures was impressive, but the undertreatment of diagnosed cases highlighted gaps in patient management. Only 10% of patients who had fractures flagged and reported by radiologists received osteoporosis treatment within a year. 

The takeaway here is that AI, while helping flag potential issues, isn’t a standalone solution. Its true value will come from its integration into systems that ensure follow-up care and appropriate treatment based on flagged findings. AI can help clinicians identify potential cases, but improving patient outcomes requires coordinated efforts throughout the healthcare system. 

AI’s Role in Spinal Fracture Care and the Larger Process

AI is becoming a key tool for radiologists, helping them diagnose fractures faster and more accurately. As health systems continue integrating AI into clinical workflows, we can expect it to play an even more significant role in improving spine care. 

But as these studies show, AI is just one piece of the puzzle. The true impact will come when it’s fully integrated into processes that ensure flagged findings lead to timely interventions and appropriate patient care.

Click here to learn how Aidoc is impacting radiology workflows today.

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Andrew MacLean

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