- A new study suggests that a stroke clinical decision support system (CDSS), which uses artificial intelligence (AI)-assisted imaging, can help significantly reduce the risk of vascular events.
- The researchers suggest that the AI tool is a safe intervention that provides the additional benefits of lower cost and greater sustainability.
- In a large study, an AI-based system improved stroke care and outcomes, supporting its potential as a scalable tool for routine stroke care, particularly in resource-limited settings.
Stroke is an important global health concern and the leading cause of disability and death in the United States.
The evidence suggests this
Physicians play an important role in preventing recurrent stroke. Typically, this occurs through the implementation of effective strategies, such as prevention plans, regular patient reviews, and lifestyle interventions.
To help with this, physicians may consider a clinical decision support system (CDSS). These systems can help healthcare institutions analyze data from electronic health records and make recommendations by sending alerts and reminders to doctors in real time.
A potential area of CDSS is to assist clinicians in complex decision-making processes for stroke prevention. However, many tools that use AI have not been rigorously evaluated, limiting their use.
Now, a major study has been published
The findings suggest that such systems could offer a scalable and cost-effective way to improve stroke management, particularly in areas with limited healthcare resources.
The use of AI technologies is increasingly being explored in healthcare, particularly for disease diagnosis, outcome prediction, and clinical decision-making support.
However, many AI tools designed for stroke care have yet to undergo rigorous evaluation in real-world clinical settings, limiting their widespread adoption.
To address this, researchers in China conducted a large-scale trial to assess whether AI-assisted CDSS could improve the quality of care and patient outcomes in daily practice.
The system analyzes brain scans to classify the causes of stroke and combines this with evidence-based treatment recommendations tailored to individual patients.
The research team suggests that the AI-based tool is associated with a significant reduction in subsequent vascular events compared to standard care.
Christopher Yee, MD, a board-certified vascular surgeon at Memorial Care Orange Beach Medical Center in Fountain Valley, CA, who was not involved in the study, suggests how AI could fit into stroke management.
“This study is the first of its kind to use AI for stroke care as a diagnostic aid that can improve the quality of care and reduce the recurrence of stroke,” Ye said.
“In this study, CDSS did more than read images: it included AI-assisted imaging, stroke cause classification, reminders for needed assessments, and guideline-based treatment recommendations.”
“The biggest takeaway is that a well-integrated CDSS can help clinicians provide evidence-based stroke care. It also helps guide interventionalists to better outcomes by improving the quality of stroke care and reducing long-term neurological events.”
– Christopher Yee, MD
The larger study involved more than 21,000 participants with acute ischemic stroke admitted to 77 hospitals in China within 7 days of symptom onset. The average age of these people was 67, and only one-third were women.
Between January 2021 and June 2023, 11,054 people received treatment at 38 hospitals supported by AI-based CDSS. Another 10,549 participants received routine medical care at 39 hospitals.
Physicians in the intervention group were trained to use the system. The CDSS incorporates a range of patient factors, including age, medical history, lifestyle, and hospital characteristics, when generating recommendations.
The study found that participants whose care was supported by CDSS experienced fewer new neurological events at multiple follow-up points. These included recurrent stroke, heart attack, or related death.
At 3 months, 2.9% (320 of 11,054) in the intervention group experienced a new neurological event, compared with 3.9% (416 of 10,549) in the control group, representing a 26% relative reduction.
This benefit persisted at 12 months, with event rates of 4% in the intervention group (440 of 11,054) versus 5.5% in the control group (576 of 10,549), representing a 27% reduction.
The research team also found that measures of quality of care were slightly higher in the intervention group, with a performance score of 91.4% compared to 89.8% in the usual care group.
Notably, the researchers add that the use of AI systems does not appear to increase the risks. There were no significant differences between groups in disability, overall mortality, or bleeding disorders at 3, 6, or 12 months.
When asked how clinically meaningful these improvements in quality-of-care measures were, Yee told us: “Moderate overall, but meaningful in the domains that matter most. The composite quality score improved from 89.8% to 91.4%, which in itself is not dramatic.”
“But several individual measures improved significantly, including dual antiplatelet use, anticoagulation for atrial fibrillation, dysphagia screening, and DVT prevention,” he noted. “These are not trivial process standards; they are directly related to secondary prevention and complication prevention.”
“The fact that recurrent neurological events fell from 3.9% to 2.9% at 3 months makes the quality gains feel clinical rather than cosmetic,” Yee emphasized.
The authors note that the trial randomized hospitals rather than individual patients. This means that differences in care practices and out-of-hospital follow-up can affect outcomes.
However, the researchers emphasize that the system was easy to integrate into existing hospital infrastructure and required relatively little training.
“The major hurdles are likely to be workflow integration, interoperability, imaging standardization, technical support, and clinician acceptance.” Today’s medical news.
“This system is integrated into the hospital information system, EMR [electronic medical record]and PACS [picture archiving and communication system]and physicians received training prior to rollout, which takes into account infrastructure and regulatory commitments,” he continued.
“The paper also notes that hospitals already struggle with insufficient resources and heavy physician workloads, which are really settings where implementation can be very difficult even if the tool has potential value,” Yee said.
“The next challenge is not to prove that AI can help, but to make it portable, scalable, affordable and easy to trust in different practice environments,” he added.
The researchers suggest that AI-powered CDSS can serve as a comprehensive management tool, supporting in-hospital care and secondary prevention strategies.
They add that this could represent a promising approach to delivering high-quality stroke care at scale, particularly in resource-limited settings with a high burden of cerebrovascular disease.
As healthcare systems continue to explore the potential role of AI, studies like this one show that such tools may offer measurable benefits in real-world clinical practice.
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