AI-Enabled Point-of-Care Ultrasound (POCUS) in Trauma and Critical Care

Artificial intelligence is rapidly transforming point-of-care ultrasound (POCUS) across trauma, emergency, and critical care settings, extending the capabilities of bedside imaging for acute diagnosis and clinical decision-making. Historically, the adoption of POCUS has been limited by variability in operator skill, resource constraints, and the logistical challenges of integrating imaging into fast-paced workflows in emergency departments, ICUs, and prehospital environments. The advent of AI—encompassing both interpretive algorithms and real-time acquisition support—now enables automated detection of key findings (such as free fluid on FAST exams, pneumothorax or effusions on lung ultrasound, and DVT on vascular studies), workflow optimization, and enhanced triage capabilities. Recent clinically validated studies have shown that AI guidance can improve probe positioning, yield higher-quality scans even for non-specialists, and facilitate robust, edge-deployed solutions for low-bandwidth or low-resource contexts. There is heightened interest in AI applications that reduce skill gaps, support pediatric imaging, streamline critical care throughput, and expand the reach of POCUS in both high- and low-resource settings. However, the field still requires rigorous clinical validation and workflow evaluation, as simulation-only studies are insufficient for safe adoption and translation.

This Research Topic calls for original research and translational studies focused on methods and rigorously validated applications of AI for POCUS in trauma, emergency, and critical care environments. Relevant areas include advances in AI-guided probe positioning and quality assurance, automated interpretation of abdominal (FAST), lung, vascular, and limited cardiac studies, and workflow solutions that enable real-time clinical decision support. Authors are encouraged to address deployment challenges such as edge-device optimization, performance in bandwidth-constrained settings, and adaptations for low- and middle-income countries or pediatric populations. Particular emphasis is placed on studies that present prospective validation, workflow improvement, or end-user acceptability in real-world settings, advancing the field beyond retrospective or simulation-only development.

This Research Topic encourages evidence-based, clinically validated research in AI-enabled POCUS, with a focus on methods and applications that directly address emergency department, ICU, prehospital, or global health needs. Simulation-only reports are considered out of scope unless paired with prospective or real-world workflow evaluation. We welcome articles addressing, but not limited to, the following themes:

· AI guidance for probe positioning and acquisition quality assurance

· Automated FAST interpretation for detection of free fluid

· Lung ultrasound AI decision support (pneumothorax, edema, pneumonia)

· Automated vascular ultrasound (DVT) detection and assistance

· Echocardiography triage for shock using basic views and AI inference

· Edge deployment, low-bandwidth operation, and robustness in adverse environments

· Pediatric POCUS workflows and validation

· AI-enhanced POCUS in low-resource or global health implementations (aligned with SDG 3)

· Prospective clinical trials, workflow integration, and user adoption studies

Manuscripts must include clinical validation and/or rigorous workflow evaluation; simulation-only studies without demonstrated clinical relevance or deployment are considered out of scope.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review

Keywords: Point-of-care ultrasound (POCUS), Artificial intelligence, Emergency medicine, Critical care medicine, Clinical validation, Automated diagnostics, Edge deployment

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.