Purpose: Point-of-care ultrasound (POCUS) is a sensitive, safe, and efficient tool used in many clinical settings and is an essential part of medical education in the United States. Numerous studies present improved diagnostic performances and positive clinical outcomes among POCUS users. However, others stress the degree to which the modality is user-dependent, rendering high-quality POCUS training necessary in medical education. In this study, the authors aimed to investigate the potential of an artificial intelligence (AI) based quality indicator tool as a teaching device for cardiac POCUS performance. Methods: The authors integrated the quality indicator tool into the pre-clinical cardiac ultrasound course for 4th-year medical students and analyzed their performances. The analysis included 60 students who were assigned to one of two groups as follows: the intervention group using the AI-based quality indicator tool and the control group. Quality indicator users utilized the tool during both the course and the final test. At the end of the course, the authors tested the standard echocardiographic views, and an experienced clinician blindly graded the recorded clips. Results were analyzed and compared between the groups. Results: The results showed an advantage in quality indictor users’ median overall scores (P = 0.002) with a relative risk of 2.3 (95% CI: 1.10, 4.93, P = 0.03) for obtaining correct cardiac views. In addition, quality indicator users also had a statistically significant advantage in the overall image quality in various cardiac views. Conclusions: The AI-based quality indicator improved cardiac ultrasound performances among medical students who were trained with it compared to the control group, even in cardiac views in which the indicator was inactive. Performance scores, as well as image quality, were better in the AI-based group. Such tools can potentially enhance ultrasound training, warranting the expansion of the application to more views and prompting further studies on long-term learning effects.

Point-of-care ultrasound is rapidly gaining traction in clinical practice, including primary care. Yet, logistical challenges and geographical isolation hinder skill acquisition. Concurrently, an evidentiary gap exists concerning such guidance's effectiveness and optimal implementation in these settings. We developed a lung point-of-care ultrasound (POCUS) curriculum for primary care physicians in a rural, medically underserved region of the south of Israel. The course included recorded lectures, pre-course assessments, hands-on training, post-workshop lectures, and individual practice. To evaluate our course, we measured learning outcomes and physicians’ proficiency in different lung POCUS domains using hands-on technique assessment and gathered feedback on the course with a multi-modal perception approach: an original written pre- and post-perception and usage questionnaire. Fifty primary care physicians (PCPs) showed significant improvement in hands-on skills, increasing from 6 to 76% proficiency (p < 0.001), and in identifying normal versus abnormal views, improving from 54 to 74% accuracy (p < 0.001). Ten weeks after training, primary care physicians reported greater comfort using lung ultrasound, rising from 10 to 54% (p < 0.001), and improved grasp of its potential and limits, increasing from 27.5% to 84% (p < 0.001). Weekly usage increased from none to 50%, and the number of primary care physicians not using at all decreased from 72 to 26% (p < 0.001). A two-day focused in-person and remote self-learning lung-POCUS training significantly improved primary care physicians' lung ultrasound skills, comfort, and implementation.

Current evaluations of large language models (LLMs) in healthcare have largely emphasized theoretical benchmarks and clinician oversight, with limited exploration of real-world physician-AI interaction. In this two-stage prospective study, we assessed physician satisfaction with LLM-generated responses to real clinical queries. This study did not evaluate clinical accuracy, patient outcomes, or patient safety. In the first unblinded stage, physicians used three models - a general-purpose model (GPT-4o), a reasoning-focused model (GPT-o1), and a healthcare-specific model (OpenEvidence) - to address 25 clinical dilemmas - and rated the quality of the responses. In the second blinded stage, the same physicians evaluated responses generated either by an LLM or by a human alone, without knowledge of the source. Across 100 real-world medical responses, median physician scores on a 5-point Likert scale were comparable between unblinded and blinded evaluations (p = 0.90). Satisfaction was not associated with physicians’ resistance to change, nor did it correlate with the accuracy or relevance of cited literature. These findings suggest that physicians did not favor information generated by LLMs over externally provided responses, and that clinician satisfaction alone may not serve as a reliable proxy for validating decision support tools.

Background & Need for Innovation: Point-of-care ultrasound (POCUS) has transformed bedside diagnostics, yet its operator-dependent nature and lack of structured training remain significant barriers. To address these challenges, Ben Gurion University (BGU) developed a longitudinal six-year POCUS curriculum, emphasizing early integration, competency-based training, and scalable educational strategies to enhance medical education and patient care. Goal of Innovation: To implement a structured and scalable POCUS curriculum that progressively builds technical proficiency, clinical judgment, and diagnostic accuracy, ensuring medical students effectively integrate POCUS into clinical practice. Steps Taken for Development and Implementation: The curriculum incorporates hands-on training, self-directed learning, a structured spiral approach, and peer-led instruction. Early exposure in physics and anatomy courses establishes a foundation, progressing to bedside applications in clinical years. Advanced technologies, including AI-driven feedback and telemedicine, enhance skill retention and address faculty shortages by providing scalable solutions for ongoing assessment and feedback. Evaluation of Innovation: Since its implementation in 2014, the program has trained hundreds of students, with longitudinal proficiency data from over 700 students. Internal studies have demonstrated that self-directed learning modules match or exceed in-person instruction for ultrasound skill acquisition, AI-driven feedback enhances image acquisition, and early clinical integration of POCUS positively influences patient care. Preliminary findings suggest that telemedicine-based instructor feedback improves cardiac ultrasound proficiency over time, and AI-assisted probe manipulation and self-learning with ultrasound simulators may further optimize training without requiring in-person instruction. Critical Reflection: A structured longitudinal approach ensures progressive skill acquisition while addressing faculty shortages and training limitations. Cost-effective strategies, such as peer-led instruction, AI feedback, and telemedicine, support skill development and sustainability. Emphasizing clinical integration ensures students learn to use POCUS as a targeted diagnostic adjunct rather than a broad screening tool, reinforcing its role as an essential skill in modern medical education.

Abdominal pain is a leading cause of primary care visits and emergency department admissions. The recent surge in the implementation of point-of-care ultrasound into primary care underscores the necessity for specialized training to enhance the expertise of primary care physicians and foster a positive attitude toward its routine use in clinical activities. This prospective cohort study, conducted between March and August 2023 at Ben Gurion University, introduced an integrative abdominal ultrasound program for 48 participating primary care physicians with no prior formal experience in abdominal ultrasound. Physicians' knowledge, practical skills, and attitudes towards abdominal ultrasound integration were evaluated using a pre/post-course clips-based pathology test, a hands-on exam immediately following the course, and a survey conducted ten weeks later. Post-course evaluations showed an improvement in primary care physicians' proficiency with hands-on skills, increasing from 26 to 69% (p < 0.001), with increased comfort using abdominal ultrasound (from 0 to 42%, p < 0.001) and enhanced understanding of its capabilities and limitations (from 0 to 58%, p < 0.001). Pattern recognition skills, assessed through clips, presented a notable rise from an average of 26% to 69% (p < 0.001). Ten weeks following the training, an increase in its utilization was observed; weekly usage rose from zero to 44%, and the proportion not using it declined from 94 to 19% (p < 0.001, p < 0.001, respectively). An integrative two-day training program increases the application of abdominal bedside ultrasound in clinical settings, demonstrating the effectiveness of combining practical training with flexible, theoretical learning.

Purpose: Point-of-care ultrasonography (PoCUS) has improved the diagnostic capacity of medical conditions; however, integrating it into medical curricula is constrained by cost, time, accessibility, and teaching style variability. This study examines whether simulator-based self-learning for cardiac PoCUS is noninferior to instructor-guided teaching. Method: This randomized controlled trial, conducted at Ben-Gurion University of the Negev, enrolled 116 medical students as part of the medical school’s PoCUS curriculum. Participants were randomly assigned to a simulator-based self-learning or conventional instructor-guided teaching group. After training, which took place January 29 to February 22, 2023, participants completed exams on February 23, 2023, assessing their abilities to obtain specific cardiac views, capture images of quality, and correctly identify common cardiac pathologies. Results: Of 116 participants, 57 (49.1%) were categorized into the instructor-guided group and 59 (50.9%) the self-learning group. Participants in the self-learning group had higher total test scores compared with the instructor-guided group (81.6% vs 77.2%, P = .30), with only the apical 2-chamber view reaching statistical significance in favor of the self-learning group (81.3% vs 68.3%, P = .04). The self-learning group also scored higher on image quality, but the difference was not statistically significant (59.5% vs 55.6%, P = .26). There was no significant difference in total scores for cardiac pathology identification (93.5% in the self-learning group vs 94.7% in the instructor-guided group, P = .81). A multivariable logistic regression presented no significant difference in achieving an above median score when adjusted for gender, chest anatomy academic grade, and prior PoCUS training (adjusted odds ratio, 1.55; 95% CI, 0.69–3.53; P = .30). Conclusions: This study suggests that the self-learning approach is noninferior to instructor-guided teaching for cardiac ultrasonography training. Incorporating these programs into medical curricula may enhance the quantity and proficiency of PoCUS operators, improving diagnostic capabilities and treatment outcomes across medical specialties.