Compared to other major medical imaging modalities such as X-ray, computed tomography (CT), and magnetic resonance imaging, medical ultrasound (US) has unique attributes that make it the preferred modality for many clinical applications. In particular, US is nonionizing, portable, and provides real-time imaging, with adequate spatial and depth resolution to visualize tissue dynamics. The ability to measure Doppler information is also important, particularly for measuring blood flows. The small size of US transducers is a key attribute for intravascular applications. In addition, accessibility has been increased with the use of portable US, which continues to move toward a smaller footprint and lower cost. Nowadays, some US probes can even be directly connected to a phone or tablet. On the other hand, US also has unique challenges, particularly in that image quality is highly dependent on the operator’s skill in acquiring images based on the proper position, orientation, and probe pressure. Additional challenges that further require operator skill include the presence of noise, artifacts, limited field of view, difficulty in imaging structures behind bone and air, and device variability across manufacturers. Sonographers become highly proficient through extensive training and long experience, but high intra- and interobserver variability remains. This skill dependence has limited the wider use of US by healthcare providers who are not US imaging specialists. Recent advances in machine learning (ML) have been increasingly applied to medical US (Brattain, Telfer, Dhyani, Grajo, & Samir, 2018), with a goal of reducing intra- and interobserver variability as well as interpretation time. As progress toward these goals is made, US use by nonspecialists is expected to proliferate, including nurses at the bedside or medics in the field. The acceleration in ML applications for medical US can be seen from the increasing number of publications (Fig. 8.1) and Food and Drug Administration (FDA) approvals (Table 8.1) in the past few years. Fig. 8.1 shows that cardiovascular applications (spanning the heart, brain and vessels) have received the most attention, compared to other organs. Table 8.1 shows that pace of US FDA-cleared artificial intelligence (AI) products that combine AI and ultrasound is accelerating. Of note, many of the products have been approved over the last couple of years. Companies such as Butterfly Network (Guilford, CT) have also demonstrated AI-driven applications for portable ultrasound and more FDA clearances are expected to be published. The goals of this chapter are to highlight the recent progress, as well as the current challenges and future opportunities. Specifically, this chapter addresses topics such as the following: (1) what is the current state of machine learning for medical US application, both in research and commercially; (2) what applications are receiving the most attention and have performance improvements been quantified; (3) how do ML solutions fit in an overall workflow; and (4) what open-source datasets are available for the broader community to contribute to progress in this field. The focus is on cardiovascular applications (Section Cardiovascular/echocardiography), but common themes and differences for other applications for medical US are also summarized (Section Breast, liver, and thyroid ultrasound). A discussion is offered in Discussion and outlook section.