Remote Mentoring and AI in POCUS Training: Bridging the Gap Through Technology and Collaboration

The integration of point-of-care ultrasound (POCUS) into medical education is hampered by a critical shortage of expert trainers. This article proposes a transformative paradigm that leverages remote mentoring technologies and artificial intelligence (AI) to create a scalable, sustainable and effective learning ecosystem for medical students.

Introduction

Point-of-care ultrasound (POCUS) has emerged as a transformative tool in modern medicine, enabling clinicians to make rapid, time-critical decisions at the bedside. From assessing fluid responsiveness in critically ill patients to identifying pneumothorax in trauma settings, POCUS has become an essential competency across multiple medical specialties (Davis et al. 2018). Yet despite its clinical importance, POCUS remains conspicuously absent from many medical school curricula; where it is taught, the quality and consistency of training varies dramatically (Hoppmann et al. 2011).

The fundamental challenge is that of scale and expertise. Traditional POCUS training relies on a hands-on, apprenticeship-based model in which an experienced clinician directly supervises a learner at the bedside. This approach is pedagogically sound—there is no substitute for real-time feedback and guided practice—but it is crucially limited by the availability of expert instructors. Medical schools worldwide face a critical shortage of faculty members with both the expertise to teach POCUS and the time to dedicate to formal education. This creates a paradoxical situation: POCUS is increasingly essential for clinical practice, yet medical students graduate without formal training in this skill.

The consequences of this gap are potentially significant. Newly qualified physicians entering residency programmes often lack foundational POCUS knowledge, requiring additional training that diverts resources from other educational priorities. More concerning, the absence of standardised, high-quality POCUS training in medical school means that learners who do receive instruction may learn from instructors with variable expertise, potentially ingraining poor technique or misinterpretation patterns that are difficult to correct later.

This article presents a framework for potentially addressing this challenge through the strategic integration of three elements: peer-assisted learning (PAL), remote mentoring technologies and AI. Using real-world examples, we demonstrate how these elements can be combined to create a scalable, sustainable and educationally sound approach to POCUS training for medical students.

The Challenge: Scalability and Quality in POCUS Education

The Apprenticeship Model and Its Limitations

The traditional apprenticeship model, effective for developing procedural skills, faces limitations for large-scale POCUS education. It is labour-intensive, requiring multiple expert instructors to personally supervise a small number of students per session—unfeasible for institutions with many students. Furthermore, training quality varies with the instructor's expertise and teaching ability. Unlike standardised procedures, POCUS requires technical skill, image interpretation and clinical reasoning, which means that an instructor may excel in one area but not be able to teach effectively in the others. Also, this model is inflexible, demanding simultaneous presence of the instructor and learners, which is impractical given scheduling conflicts of both parties, as well as the lack of asynchronous learning options.

Peer-Assisted Learning: A Partial Solution

In response to these limitations, many medical schools have adopted peer-assisted learning (PAL) models for POCUS training. In these programmes, senior medical students who have already achieved a level of proficiency in POCUS teach their junior peers. The advantages of this approach are substantial. PAL is cost-effective, as it does not require the time of senior faculty members. It is also pedagogically sound in many respects: junior students often find it easier to learn from peers who are only slightly ahead of them, as peers may better understand the learning challenges and can communicate in accessible language (Brierley et al. 2022). Additionally, PAL fosters a collaborative learning environment and builds a sense of community among students. Recent meta-analyses have demonstrated that PAL can significantly enhance students' academic performance and clinical skills development (Li et al. 2025). Furthermore, PAL has been shown to reduce student stress and anxiety while improving self-efficacy (Feng et al. 2024).

Two exemplary PAL initiatives are Sono4you Graz in Austria and UltraLearn in the United Kingdom. Both programmes have demonstrated the value of student-led POCUS training and have gained international recognition for their innovative approaches.

Case Study: Sono4you Graz

Sono4you Graz, based at the Medical University of Graz in Austria, represents one of the most successful PAL initiatives in medical education. Founded in 2013 by a group of enthusiastic medical students, Sono4you Graz has grown from a small, informal teaching group into a structured, multi-year curriculum that has trained hundreds of medical students. The programme is organised hierarchically, with senior students serving as instructors, intermediate students as teaching assistants and junior students as learners. It is important to emphasise that involvement is completely voluntary and there is no financial remuneration for either trainers or learners.

The Sono4you Graz curriculum covers core POCUS applications including cardiac assessment, lung ultrasound, abdominal imaging and vascular access. Teaching sessions are conducted in a dedicated ultrasound laboratory equipped with multiple ultrasound machines. The programme emphasises hands-on practice, with students spending significant time acquiring images. Assessment is rigorous; students must demonstrate competency in image acquisition and interpretation before progressing to the next level.

The remarkable success of the Sono4you Graz programme can be attributed to several factors. First, the programme benefits from institutional support, with the Medical University of Graz providing dedicated space, equipment and administrative resources. In addition, the curriculum is well-structured and evidence-based, drawing on international guidelines and best practices. The programme has established a strong culture of excellence, with peer instructors taking their teaching responsibilities seriously and investing time in developing their pedagogical skills. Crucially, Sono4you Graz has created a sense of ownership and pride among participants, with a careful selection process and its student members viewing the programme as a valued part of their medical education (Pierce et al. 2024). Indeed, its success has meant that sister groups have been set up in Hamburg and Milan among others.

Despite the excellent track record in training over the years, the Sono4you Graz team astutely recognised that the quality of instruction depends on the expertise and teaching ability of individual peer instructors. Hence expert oversight is present in the form of a nominated clinician so as to minimise the risk of errors or suboptimal techniques propagating from one cohort of students to the next. A further limitation is that the programme is geographically restricted to Graz, preventing students in other institutions from benefitting from the expertise developed there. Finally, its sustainability relies heavily on the continued enthusiasm and commitment of student volunteers, which can be unpredictable, especially since it is an unpaid role.

Case Study: UltraLearn

UltraLearn, based in the United Kingdom, takes a similar approach to peer-assisted POCUS training. Beginning at a single institution, the programme now operates as a distributed network of student-led ultrasound clubs across multiple medical schools. The programme provides standardised curricula, teaching resources and assessment tools that participating institutions can adapt to their local context.

The UltraLearn model emphasises accessibility and inclusivity. It offers both in-person and online learning opportunities, recognising that not all students have equal access to hands-on training. Online modules cover fundamental concepts such as ultrasound physics, probe handling and image interpretation, while in-person sessions focus on hands-on skill development. This blended approach allows students to learn at their own pace and to access high-quality educational content regardless of their geographic location.

The Quality Control Challenge

As discussed previously, PAL models like Sono4you Graz and UltraLearn can face a quality control challenge: lack of expert oversight can compromise consistency and quality. Junior students cannot verify the accuracy of peer instructors' technique or interpretation, with a real risk of error propagation.

This issue of quality control is critical—POCUS interpretation errors in clinical practice, such as missing pneumothorax or DVT, can severely impact patient outcomes. Therefore, any POCUS programme must incorporate mechanisms to ensure correct technique and accurate interpretation skills, especially since training relies heavily on simulation, limiting exposure to clinical nuances.

The Solution: Remote Mentoring for Expert Oversight

Technological Innovation Enables Remote Supervision

Technology has made it possible to integrate expert supervision into peer-led teaching groups without requiring the physical presence of an expert instructor. The key innovation is real-time telemedicine platforms that allow remote experts to view both the ultrasound image and the learner's hand position and scanning technique in real-time. This enables the expert to provide immediate feedback on image acquisition, probe positioning and interpretation.

The feasibility and effectiveness of this approach have been rigorously demonstrated in the REMOTE Study conducted by Conway and colleagues in the UK. This landmark study evaluated the use of real-time remote mentoring for echocardiography in intensive care settings, but its findings are directly applicable to POCUS training for medical students. More broadly, telemedicine has emerged as a powerful tool for medical education, particularly in the context of clinical supervision and skill development (Jumreornvong et al. 2020). Virtual teaching modalities have demonstrated effectiveness comparable to traditional in-person instruction, with particular advantages for accessibility and scalability (Waseh and Dicker 2019).

The REMOTE Study: Evidence for Remote Mentoring

The study used a mixed-methods design, combining quantitative and qualitative data to assess the technical feasibility and educational effectiveness of remote mentoring (Conway et al. 2025). Quantitative metrics included image quality, report accuracy and teaching effectiveness; qualitative data was obtained from interviews and focus groups on participant experiences.

Quantitative results were strong: high image quality, impressive report accuracy and all sessions completed successfully. This suggests remote mentoring provides high-quality teaching comparable to or better than traditional methods.

Qualitative analysis revealed four key themes. First, the "accessibility of expertise" democratised expert knowledge, especially benefiting learners in resource-limited settings. Second, the "educational value" was high, with participants valuing the real-time feedback and immediate clarification. Third, "technical considerations" were crucial, with reliable connectivity and user-friendly technology being essential, though technical issues were rare. Fourth, "implementation challenges" like scheduling and the need for training were identified, but these were deemed manageable with proper planning.

Applying the REMOTE Model to Student POCUS Training

The REMOTE Study's success in remote intensive care practitioner training for advanced echocardiography has significant implications for medical student POCUS. Since the technical demands are lower for basic POCUS, remote expert mentoring can readily support PAL.

Traditional PAL lacks quality control, potentially missing errors. Remote mentoring, however, allows an expert (e.g., cardiologist) to virtually "drop in" on peer sessions. They can observe image acquisition and technique, providing real-time feedback to correct errors and reinforce concepts, which has the added advantage of improving the peer instructor's skills.

This hybrid model—remote expert oversight of local peer networks—potentially solves both the quality control issue of PAL and the scalability issue of traditional expert-led teaching. A single expert can efficiently mentor multiple peer groups across different locations. Peer instructors retain teaching ownership, and students benefit from both the accessibility of peer teaching and quality assurance of an expert trainer.

Scaling Up: Multi-Institutional Collaboration and the ADAPT Model

The ADAPT Model: Evidence for Multi-Institutional Collaboration

The ADAPT study, published in Academic Medicine (2021) by Nix and colleagues, describes a multi-institutional collaborative programme for POCUS training developed in response to COVID-19-related disruptions to medical education (Nix et al. 2021).

Fifteen US medical schools and residency programmes participated, pooling faculty to create a standardised, high-quality curriculum. The curriculum covered 20 core POCUS topics, including cardiac, lung, abdominal and procedural guidance. Teaching combined didactic instruction with hands-on learning via real-time teleguidance and incorporated gamification elements.

ADAPT yielded impressive results: educators reported a 50% reduction in preparation time, decreasing from an average of 6.2 to 3.1 hours per week.

The authors concluded that "a virtual curriculum that pools the efforts of multiple institutions nationwide was implemented rapidly and effectively while satisfying educational expectations of both learners and faculty."

Applying ADAPT to Medical Student POCUS Training

Adopting the ADAPT model for medical student POCUS training could offer significant advantages, with a consortium of medical schools collaboratively developing and delivering a standardised, high-quality POCUS curriculum.

Instruction would combine synchronous videoconference sessions, featuring experts from various institutions internationally, and asynchronous access to recordings. Hands-on training would be delivered locally via peer-teaching (like Sono4you Graz), with remote expert oversight (per the REMOTE Study).

The substantial benefits provided by this model include reduction in the burden on individual institutions by sharing expertise; consistent quality across all students through a shared, evidence-based curriculum; cost-effectiveness due to pooled resources and economies of scale, and valuable inter-institutional faculty collaboration.

The Role of AI in POCUS Education: Learning and Quality Assurance

AI enhances POCUS training by providing real-time guidance for image acquisition, supporting image interpretation and ensuring quality assurance. AI can analyse images as they are acquired, offering immediate feedback and suggesting adjustments to improve quality, thereby reducing the need for constant instructor supervision.

AI algorithms trained on large datasets aim to accelerate learning and skill development by guiding learners and reinforcing correct techniques. Deep learning systems have shown accuracy comparable to expert sonographers, making AI a valuable tool in efficient ultrasound training (Song et al. 2024).

In image interpretation, AI models can identify anatomical structures and pathologies, provide instant feedback and act as a safety net by flagging misinterpretations before they affect patient care. Such tools provide the opportunity for learners to hone their interpretative skills across a wide number of cases and fosters accuracy and confidence.

For quality assurance, AI objectively assesses image quality and interpretation, tracking learner progress and identifying areas needing improvement. When used across multiple institutions, AI helps standardise competency assessment and maintain consistent training standards.

Implementation Framework: Integrating Remote Mentoring, Multi-Institutional Collaboration and AI

A Comprehensive Model for Medical Student POCUS Training

We propose a comprehensive framework that integrates remote mentoring, multi-institutional collaboration and AI to create a scalable, sustainable and educationally sound approach to POCUS training for medical students. This framework has several key components:

1. Curriculum Development: Standardised, evidence-based POCUS curriculum developed collaboratively by multiple medical schools, progressing from foundational concepts to applied clinical skills, guided by international standards (Expert Round Table on Ultrasound in ICU 2011).
2. Blended Learning Delivery: Uses a combination of synchronous video-conference sessions with faculty experts and asynchronous materials (recorded lectures, videos, interactive modules) on a shared platform, supporting self-paced learning and improving outcomes.
3. Local Peer-Teaching with Remote Expert Oversight: Hands-on peer-teaching programmes (e.g., Sono4you Graz) are enhanced with real-time remote expert feedback and guidance via telemedicine platforms.
4. AI-Enhanced Learning and Assessment: AI algorithms offer real-time guidance on image acquisition and interpretation, automatically assessing quality and accuracy to track learner progress and identify training gaps (Mir et al. 2023).
5. Continuous Quality Improvement: Learner outcomes and programme effectiveness data are continuously collected and analysed to refine the curriculum and teaching methods (Tolsgaard et al. 2013).

Conclusion

The integration of POCUS into medical student education is not merely desirable—it is a necessity. As POCUS becomes an increasingly important tool in clinical practice, medical schools have a responsibility to ensure that their graduates are competent in this skill. However, the current infrastructure for POCUS training is insufficient to meet this challenge. The shortage of expert trainers, the limitations of traditional apprenticeship-based teaching and the variability of peer-teaching programmes all contribute to a situation in which many medical students graduate without adequate POCUS training.

The solution lies in a synergistic model that combines the strengths of PAL, remote expert mentoring and AI. PAL programmes like Sono4you Graz and UltraLearn have demonstrated the potential of student-led training, but they require expert oversight to ensure quality. Remote mentoring technologies, as validated by the REMOTE Study, enable experts to provide real-time feedback and guidance to learners across geographic distances, making expert supervision scalable and sustainable. Multi-institutional collaboration, as exemplified by the ADAPT model, allows institutions to pool resources and expertise, reducing the burden on individual institutions while ensuring consistency and quality. AI further provides tools for real-time guidance on image acquisition and interpretation, accelerating the learning curve and providing quality assurance.

By integrating these elements into a comprehensive framework, medical schools can create a scalable, sustainable and educationally sound approach to POCUS training. Students will have access to high-quality instruction from expert mentors, regardless of their geographic location or their institution's resources. Peer instructors will benefit from expert oversight and feedback, improving their own skills and teaching ability. Institutions will be able to deliver high-quality POCUS training without requiring a large, dedicated faculty. And ultimately, patients will benefit from a new generation of physicians who are competent in POCUS and can use this skill to improve diagnosis and patient outcomes.

The future of POCUS training is not in isolated, institution-specific programmes, but in collaborative, technology-enabled networks that leverage the expertise of the global medical community. By embracing this new paradigm, we can ensure that POCUS becomes a standard competency for all medical graduates, not a luxury available only to students at well-resourced institutions.

Conflict of Interest

None.