## 8 Discussion
### Potentials and the Integration of AI-Enhanced POCUS in LRS  
Due to the limited training and medical certification available in low-resource settings, diagnosing conditions using POCUS becomes difficult [83]. AI can play a crucial role in improving diagnoses through the integration of machine learning modalities with general ultrasound for pathology detection, feature extraction, and disease diagnosis [81; 84]. For example, studies by [PERSON] et al. and [PERSON] et al. utilized support vector machine (SVM) and artificial neural network (ANN) learning models, respectively, to diagnose breast masses with around 96% accuracy [85; 86]. Similarly, the employment of ANNs and SVM models can help directly diagnose cirrhosis and fatty liver disease, amongst others, in the liver as well as lesion margin in the thyroid with extremely high accuracy [87; 88; 89]. Additionally, machine learning has the ability to augment current diagnostic procedures within ultrasound technology for commonly seen pathologies such as those found in the heart or lungs [89; 90; 91]. Despite these advances, there is a significant lack of studies focusing on AI applications in POCUS. While there are advancements in AI-enhanced POCUS and some studies exploring its use in LRS, none of these studies met the inclusion criteria for this review. Nonetheless, there are notable examples of POCUS and AI integration, particularly in the areas of COVID-19 and obstetrics. For instance, [PERSON] et al. developed a deep learning model that assisted sonographers in obtaining high-quality cardiac ultrasound images in the COVID-19 intensive care unit by utilizing a model trained on the hand movements of skilled cardiac sonographers [92]. [PERSON] et al. describe a ML-based algorithm that was used to estimate fetal gestational age estimation through POCUS [92]. Similarly, [PERSON] et al. developed an AI model that estimated gestational age with an accuracy that performed similarly to trained sonographers using low-cost ultrasound technology with data obtained from volunteers in Zambia and North Carolina [93]. Additionally, [PERSON] et al. compared AI-POCUS with CT scans, and they found that AI-POCUS achieved a 94.5% accuracy in detecting CT-validated pneumonia [94]. [PERSON] et al. analyzed six common DL image classification algorithms, and found that older and less complex CNN performed the best [95]. One major challenge in developing AI algorithms for POCUS lies in the limited availability of publicly available datasets, and the diversity of POCUS applications and ultrasound equipment makes it hard to standardize datasets [24]. To address this limitation, recent work has employed techniques such as CycleGAN to generate synthetic POCUS images, enabling the classification of breast cancer with a 95% confidence interval for AUC between 93.5 and 96.6 [96]. As AI integration continues to expand in healthcare, we anticipate that these models will likely be adapted for POCUS devices in preparation for their implementation in LRS. Through this comprehensive review, it is evident that POCUS holds significant advantages for healthcare delivery in various LRS across multiple clinical specialties. The integration of AI in POCUS presents a promising opportunity to overcome existing barriers and advance the applications within LRS. Furthermore, AI-enhanced POCUS plays a crucial role in supporting the growth of telemedicine, a field that has been more increasingly adopted after the disruption of in-person hospital visits during the COVID-19 pandemic. It enables skilled clinicians to provide remote guidance and interpretation of ultrasound images, consequently facilitating remote monitoring and reducing the need for costly specialized visits. Specifically, the use of AI to help improve image quality, regardless of the user's training level, yields diagnostically relevant images [97].