Key Message

What Is the Issue?

• Dementia is a condition characterized by symptoms such as memory loss and difficulties with attention, language, and problem solving resulting from specific disruptions to brain function, reducing a person’s ability to perform everyday activities.
• Alzheimer disease (AD) is a chronic neurological degenerative disease and 1 of the most common causes of dementia, affecting an estimated 368,200 people in Canada in 2020. Mild cognitive impairment (MCI) is a clinical condition involving memory loss that can progress to dementia and is often due to underlying AD. It was estimated to affect around 917,000 people in Canada aged 60 and older in 2020.
• Disease-modifying therapies (DMTs) are being developed that target underlying pathologic processes of AD to slow disease progression, unlike contemporary treatments that focus on managing symptoms. A prominent target of these therapies is amyloid-beta, a protein known to contribute to amyloid plaques.
• Many of the anti–amyloid-beta DMTs are intended for people with early-stage AD, which includes MCI and mild dementia due to AD. However, DMTs demand more frequent health care visits and higher use of medical imaging for safe treatment and monitoring. This would greatly impact current care pathways for early-stage AD, necessitating an examination of health system readiness in Canada.

What Did Canada’s Drug Agency Do?

• Canda’s Drug Agency sought to examine health system readiness in Canada and care pathways for patients with early-stage AD in preparation for the potential use of anti–amyloid-beta DMTs by reviewing publicly available data and literature about health systems and AD treatment pathways for improving dementia care in Canada.
   

Key Message

What Is the Issue?

• Stroke is a sudden loss of neurologic function caused by poor or interrupted blood flow within the brain. It is 1 of the leading causes of death and a major cause of disability in Canada. For patients with suspected stroke, prompt evaluation using CT imaging and other tests can help to determine the type of stroke, to assess the severity of damage, and to guide treatment decisions.
• RapidAI is an artificial intelligence (AI)–enabled software platform that facilitates the viewing, processing, and analysis of CT images to aid clinicians in assessing patients with suspected stroke. Understanding the potential benefits and harms of using RapidAI is important to clarify its role in stroke detection.

What Did We Do?

• We sought to identify, synthesize, and critically appraise literature evaluating the effectiveness, accuracy, and cost-effectiveness of RapidAI for detecting large-vessel occlusion (LVO) (i.e., ischemic stroke) and intracranial hemorrhage (ICH) (i.e., hemorrhagic stroke).
• We searched key resources, including journal citation databases, and conducted a focused internet search for relevant evidence published up to July 22, 2024. We screened citations for inclusion based on predefined criteria, critically appraised the included studies, narratively summarized the findings, and assessed the certainty of evidence. Our methods were guided by the Scottish Health Technologies Group’s health technology assessment (HTA) framework.
• We highlighted and reflected on the ethical and equity implications of using RapidAI for stroke detection, found in the clinical literature, integrating these considerations throughout the review.
• We engaged a patient contributor who had experienced a hemorrhagic stroke, to learn about her experience, perspectives, and priorities. Additionally, we incorporated feedback from clinical and ethics experts, the manufacturer, and other interested parties.

What Did We Find?

• We found 2 cohort studies and 11 diagnostic accuracy studies that assessed the effectiveness and accuracy of RapidAI for detecting stroke. Among these, 3 studies evaluated RapidAI as it is intended to be used in clinical practice (i.e., to complement clinician interpretation of CT images), while the remaining 10 studies assessed RapidAI as a standalone intervention.
• The patient contributor identified important outcomes for stroke care, including improving speed and accuracy of diagnosis, minimizing the damaging effects of stroke, and reducing mortality rates. She also highlighted ethical considerations regarding the use of AI in health care, such as providing data privacy and equitable access, as well as informing patients about the use of AI technologies in the care pathway.
• Low-certainty evidence suggests that evaluation of CT angiography images by Rapid LVO combined with clinician interpretation, compared to clinician interpretation alone, may result in clinically important reductions in radiology-report turnaround time in patients with suspected stroke. For detecting ICH, low-certainty evidence suggests that Rapid ICH combined with clinician interpretation, using clinician interpretation as a reference standard, has a sensitivity of 92% (95% confidence interval [CI], 78% to 98%) and a specificity of 100% (95% CI, 98% to 100%). However, estimates of sensitivity and specificity for detecting LVO varied, based on studies using different modules of RapidAI as a standalone intervention, providing only indirect accuracy data.
• The effects of RapidAI on other time-to-intervention metrics, measures of physical and cognitive function, and response to therapy (e.g., reperfusion rates) were very uncertain. We did not identify any evidence on the effects of RapidAI on many important clinical outcomes, including patient harms, mortality, health-related quality of life, length of hospital stay, or health care resource implications.
• We did not find any studies on the cost-effectiveness of RapidAI for detecting stroke that met our selection criteria for this review.
• Ethical and equity considerations related to patient autonomy, privacy, transparency, access, and algorithmic bias have implications across the technology life cycle when using RapidAI for detecting stroke.

What Does This Mean?

• RapidAI has the potential to improve acute stroke care by creating efficiencies in the diagnostic process. However, the impact of RapidAI on many outcomes, including those that are important to patients, is uncertain due to limitations of the available evidence.
• To improve the certainty of findings, there is a need for evidence from robustly conducted studies at lower risk of bias that enrol diverse patient populations and measure outcomes that are important to patients, with improved reporting.
• The cost-effectiveness of RapidAI for stroke detection is currently unknown.
• In addition to the evidence on the effectiveness and accuracy of RapidAI for detecting stroke, decision-makers may wish to reflect on the ethical and equity considerations that arise during the deployment of AI-enabled technologies, such as those related to autonomy, privacy, transparency, and explainability of machine-learning models, and the need for considerations related to equity and access in their design, development, and deployment.