From Echocardiography to CT/MRI: Lessons for AI Implementation in Cardiovascular Imaging in LMICs-A Systematic Review...

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiovascular Diseases

Background:

• Cardiovascular imaging plays a crucial role in diagnosing heart conditions.
• Low- and middle-income countries (LMICs) face challenges in accessing advanced diagnostic tools.
• Artificial intelligence (AI) offers potential solutions to improve healthcare accessibility and efficiency.

Purpose of the Study:

• To synthesize evidence on AI adoption in cardiovascular imaging within LMICs.
• To evaluate AI's diagnostic performance, implementation barriers, and solutions.
• To identify pathways for AI integration in global cardiovascular imaging.

Main Methods:

• Systematic review of major databases (PubMed, Embase, Cochrane, Web of Science, Scopus).
• Screening of studies on AI in echocardiography, cardiac CT, and cardiac MRI in LMICs.
• Narrative synthesis of diagnostic outcomes, challenges, and enabling factors.

Main Results:

• AI methods achieved over 90% accuracy in detecting various heart conditions.
• AI enabled task shifting to non-expert operators, improving workforce efficiency.
• Key challenges include limited data diversity, infrastructure, and ethical concerns.

Conclusions:

• AI demonstrates significant potential to enhance cardiovascular care in LMICs.
• Sustainable AI adoption necessitates multi-center data sharing, training, and robust infrastructure.
• AI can bridge resource gaps, informing future research and policy in global cardiovascular imaging.