## 1 Introduction  
Since the 1950s, ultrasounds have been utilized for various medical applications, continuing to be an invaluable diagnostic resource [1]. The utility and penetration of ultrasound has been increasing, supported by an expanding market with an average annual growth rate of 5.9% [2]. This is further supported by the fact that the global medical ultrasound market is expected to reach USD 8.4 billion in 2023 from USD 6.3 billion in 2018 [2]. While ultrasound equipment is predominantly used in obstetrics, it plays a crucial role in other systems such as the heart, eyes, abdomen, and brain [3]. However, due to the complex and delicate nature of ultrasonography machinery, it must be operated by licensed healthcare professionals or individuals who have undergone extensive training [4]. Given high technology costs and reliable infrastructure (electricity and storage) necessities, the inaccessibility of ultrasound technology stretches further into low-resource settings. Therefore, technological advancements in ultrasound are crucial to optimize its use in low-resource settings [5]. In response, the medical device industry has pivoted its focus over the past decade towards creating ultrasounds that are more accessible to all medical professionals, regardless of certification. One such innovation has been the development of point-of-care ultrasounds (POCUS), which are more affordable, portable, and have demonstrated similar accuracy when compared with cart-based ultrasound for many clinical applications [6]. As newer versions of POCUS continue to develop, the ability to make complex diagnoses has also improved [7; 8]. For example, in emergency medicine, where capturing site-specific imaging is crucial, POCUS is applied to support rapid diagnosis [9]. Moreover, POCUS training is now integrated early in medical education with POCUS replacing the stethoscope for several medical schools in the United States [10]. Outside formal education, several qualitative-based protocols for ultrasound assessments, such as Rapid Ultrasound for Shock and Hypertension (RUSH), Focused Assessment with Sonography for Trauma (FAST), and Abdominal and cardiac evaluation with sonography in shock (ACES) have been incorporated into treatment guidelines [11]. Current knowledge about POCUS has been accumulated from developed countries in resource-rich contexts like academic and tertiary hospitals [12; 13; 14; 15; 16; 17]. While this is a good starting point, there is a critical need to understand the use of POCUS in rural settings and low-resource settings. Low-resource settings (LRS) are defined as \"geographical areas where populations have limited access to qualified healthcare providers and quality healthcare services\" [18]. LRS represent populations who can benefit from POCUS innovations since there exists a lack of imaging resources, with almost 50% of patients in rural areas requiring transfer to advanced centers for basic imaging [19]. While POCUS shows promise in LRS, barriers such as limited training, maintenance, and on-site analysis remain challenges [20]. Therefore, POCUS research has been focused on implementing and evaluating training systems to increase the usage of POCUS in practice in LRS [21]. According to [PERSON] et al., while POCUS has been utilized in short-term medical missions, training is necessary to ensure that local providers are able to utilize ultrasound machines [22]. Training with POCUS would improve the continuity of care to local patients and bridge the aforementioned gaps in access to imaging technology [22]. However, training systems present their own set of challenges. The reality is that ultrasound training is costly, time-intensive, difficult to scale up, and challenging to standardize across clinics, especially given the wide variety of applications and user groups involved [23; 24]. To address this issue, artificial intelligence (AI) provides a unique potential to fill the gap.