AI-enabled point-of-care ultrasound enhances cardiovascular image acquisition and interpretation, but widespread adoption faces challenges regarding algorithm generalizability, bias, and data privacy.
AI-enabled point-of-care ultrasound holds transformative potential for cardiovascular diagnostics by enhancing image acquisition and expanding access, provided challenges like algorithm bias and data privacy are addressed.
PURPOSE OF REVIEW: The integration of artificial intelligence (AI) with point-of-care ultrasound (POCUS) is transforming cardiovascular diagnostics by enhancing image acquisition, interpretation, and workflow efficiency. These advancements hold promise in expanding access to cardiovascular imaging in resource-limited settings and enabling early disease detection through screening applications. This review explores the opportunities and challenges of AI-enabled POCUS as it reshapes the landscape of cardiovascular imaging. RECENT FINDINGS: AI-enabled systems can reduce operator dependency, improve image quality, and support clinicians-both novice and experienced-in capturing diagnostically valuable images, ultimately promoting consistency across diverse clinical environments. However, widespread adoption faces significant challenges, including concerns around algorithm generalizability, bias, explainability, clinician trust, and data privacy. Addressing these issues through standardized development, ethical oversight, and clinician-AI collaboration will be critical to safe and effective implementation. Looking ahead, emerging innovations-such as autonomous scanning, real-time predictive analytics, tele-ultrasound, and patient-performed imaging-underscore the transformative potential of AI-enabled POCUS in reshaping cardiovascular care and advancing equitable healthcare delivery worldwide.
East et al. (Mon,) conducted a review in Cardiovascular diagnostics. AI-enabled point-of-care ultrasound (POCUS) was evaluated. AI-enabled point-of-care ultrasound enhances cardiovascular image acquisition and interpretation, but widespread adoption faces challenges regarding algorithm generalizability, bias, and data privacy.