Beyond Computer-Aided Diagnosis: Artificial Intelligence as a “Digital Mentor” for POCUS Image Acquisition and Quality Assurance: A Narrative Review

TL;DR

This paper reviews how AI can act as a digital mentor to improve ultrasound image quality and reduce reliance on expert feedback.

Contribution

The novel contribution is proposing AI as an upstream 'digital mentor' to enhance POCUS image acquisition and QA processes.

Findings

AI-guided acquisition helps novices produce diagnostically adequate images after brief training.

Automated QA scoring could reduce expert workload and feedback cycles.

Integration of AI with FOAMed resources and simulation is proposed for safe deployment.

Abstract

Point-of-care ultrasound (POCUS) is portable and radiation-free, but its clinical reliability is constrained by operator-dependent image acquisition and the limited scalability of expert quality assurance (QA) review. As handheld devices proliferate faster than mentorship capacity, trainees increasingly rely on heterogeneous free open access medical education (FOAMed) resources that rarely provide real-time psychomotor feedback. We conducted a structured narrative review (MEDLINE, Embase, Scopus, and Web of Science; last searched on 23 February 2026), with searches performed by H.H. and independently checked by J.J.P. (both POCUS-trained clinicians). After screening, 31 studies were included. We synthesized evidence on artificial intelligence (AI) systems that support bedside image acquisition and automate QA. The primary synthesis centered on key prospective or comparative clinical evaluations of AI-guided acquisition across echocardiography, focused assessment with sonography in trauma, abdominal aortic aneurysm screening, and lung ultrasound, complemented by peer-reviewed studies of FOAMed appraisal tools and online resource quality. These evaluations suggest that real-time probe guidance, view recognition, anatomy labeling, and automated capture may enable novices, after brief training, to acquire diagnostically adequate images for narrowly defined tasks. Early reports of automated QA scoring and program-level triage for expert review suggest potential to reduce expert workload and shorten feedback cycles, but external validation, generalizability across devices and patient habitus, and patient-centered outcomes remain limited. Acquisition-focused AI may therefore serve as an upstream “digital mentor” to improve novice image acquisition. We propose a practical pathway that integrates curated FOAMed resources and simulation with AI-guided bedside acquisition and continuous QA governance for safe deployment.

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