# Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
## 4 Discussion  
There is a shortage of skilled and experienced US practitioners, and this is particularly significant in developing countries, where the World Health Organization estimates that many US scans are acquired by personnel with little or no formal training. As US imaging is particularly subject to errors, poor image quality, and incorrect imaging, the misinterpretation of images is a significant risk in US-based diagnosis. Therefore, advances in this field are expected to impact clinical practice significantly. AI algorithms can help reduce the steep learning curve in ultrasound scanning by assisting physicians, nurses, and technicians to perform full examinations. AI-assisted US scanning is also of great interest for military medical scenarios where US imaging needs to be used by non-skilled personnel for rapid triaging in remote environments. Even in the future, chronic patients could be able to scan themselves and send the images to specialists thanks to these technologies. There has been a significant improvement in the field of AI-assisted US scanning in the last few years, especially with the use of deep-learning methods and larger datasets. As shown in this review, for instance in Table 1, works based on deep-learning methods have used typically one or two orders of magnitude more images to train the model than previous works based on more traditional machine-learning approaches such as SVM. The use of large training datasets is a typical requirement of deep-learning methods, as they allow for the generation of more robust and generalizable models. It is interesting to note that, as shown, for instance, in Table 1, the accuracy of the published results obtained with SVM, RF, and DL methods seems to be similarly good in many cases. In fact, the accuracy achieved in the published results would be good enough for practical purposes if they showed the same performance in other real-case scenarios. However, the comparison between different publications in this field is very difficult, even when they are focused on imaging the same organ or region, as each one is applied to a different dataset with different US systems and different image quality. A detailed and fair comparison of the best implementations of each proposed AI method applied to a specific study would be very beneficial to the field. The development of these AI-based technologies has many important challenges that are expected to be addressed in the following years:  
* Deep learning systems are only as good as the curated datasets applied in their training. The dependence on the operator creates a large variability in US-acquired images, which may limit the generalizability of DL-based systems [69]. AI results depend on how the target structure is represented and defined by the examiner in the captured image [70] and, furthermore, on whether the target is correctly identified and captured. Therefore, the data used for training AI systems must be acquired by examiners with some degree of competency and experience [70]. * Training data are usually taken by a group of physicians that share the same protocols and criteria to choose the right standard views. This may create considerable differences between the imaging data collected to train AI algorithms and the data from real-world practice. Normalization of scanning and image acquisition may be required to mitigate this problem, and it could be critical for the successful future application of AI in the US. In fact, this requirement of AI tools may promote the standardization and image quality verification of US examinations performed by humans. * A lot of expert sonographer time is required to create datasets by sampling the body surface in all needed probe positions and orientations. Even in a simplified case, as the one presented in [71] for the parasternal long axis (Plax) acoustic window, it required more than 20 min per patient. * Most of the proposed approaches for AI-based ultrasound guidance in this review paper require labeled data to train the algorithms.