THE ROLE OF ARTIFICIAL INTELLIGENCE IN GUIDING POINT-OF-CARE ULTRASOUND FOR DEEP VEIN THROMBOSIS DIAGNOSIS: A SYSTEMATIC REVIEW
TL;DR
It is demonstrated that the hybrid AI-human model achieved high diagnostic performance for ruling out proximal DVT, with a sensitivity of 90-100% and a negative predictive value (NVP) of 87.5-100%.
Abstract
Deep vein thrombosis (DVT) is a significant global health burden. Point-of-care ultrasound (POCUS) enhances diagnostic access but is dependent on the operator. Artificial intelligence (AI) has been proposed to guide novices and standardize image acquisition, often within a hybrid model incorporating remote clinician review. This systematic review aimed to synthesize the diagnostic accuracy, clinical feasibility, and health economic impact of AI-guided POCUS for DVT diagnosis within AI-human hybrid models. This systematic review was conducted in accordance with PRISMA guidelines, searching databases (PubMed/Medline, Scopus, Web of Science, IEEE, and Google Scholar) for studies published between 2021 and 2025. Four studies met the eligibility criteria, assessing AI-guided POCUS systems for DVT diagnosis. These studies demonstrated that the hybrid AI-human model achieved high diagnostic performance for ruling out proximal DVT, with a sensitivity of 90-100% and a negative predictive value (NVP) of 87.5-100%. Reviewer expertise significantly impacted accuracy; emergency medicine POCUS-trained physicians outperformed general radiologists. The model was feasible for non-expert operators (e.g., nurses) with minimal training and showed potential to reduce unnecessary duplex ultrasound referrals by 29–58%, with associated workflow efficiency. AI-guided POCUS combined with mandatory clinician review forms an effective hybrid model that enhances access, standardizes quality, and safely rules out DVT. Successful real-world implementation requires targeted reviewer training, workflow integration, and further health economic and independent pragmatic evaluation.