Handheld ultrasound has not yet reached mainstream adoption but the market is still forecast to reach over $500 million by 2026. Signify Research's newly published Handheld Ultrasound Deep Dive Report 2022 shows that market revenues are estimated to have grown by approximately 30% in 2021, driven by strong growth in the US, the biggest market for handheld ultrasound. Despite the global challenges for handheld ultrasound vendors in 2022, such as rising inflation and supply chain disruptions, the handheld ultrasound market is expected to experience double-digit growth, and this is forecast to continue through to 2026. Most of the market growth will be fuelled by the increased adoption of handheld devices by new users of ultrasound, such as primary care physicians, nurses, emergency medical technicians (EMTs), and midwives. The key market trends are discussed below.

Network coding allows distributed information sources such as sensors to efficiently compress and transmit data to distributed receivers across a bandwidth-limited network. Classical network coding is largely task-agnostic -- the coding schemes mainly aim to faithfully reconstruct data at the receivers, regardless of what ultimate task the received data is used for. In this paper, we analyze a new task-driven network coding problem, where distributed receivers pass transmitted data through machine learning (ML) tasks, which provides an opportunity to improve efficiency by transmitting salient task-relevant data representations. Specifically, we formulate a task-aware network coding problem over a butterfly network in real-coordinate space, where lossy analog compression through principal component analysis (PCA) can be applied. A lower bound for the total loss function for the formulated problem is given, and necessary and sufficient conditions for achieving this lower bound are also provided. We introduce ML algorithms to solve the problem in the general case, and our evaluation demonstrates the effectiveness of task-aware network coding.

What will the market size be in 2027 and what will the growth rate be? What are the key market trends? What is driving this market? What are the challenges to market growth? Who are the key vendors in this market space?

With tech giants pouring billions of dollars into artificial intelligence projects, it's hard to see how startups can find their place and create successful business models that leverage AI. However, while fiercely competitive, the AI space is also constantly causing fundamental shifts in many sectors. And this creates the perfect environment for fast-thinking and -moving startups to carve a niche for themselves before the big players move in. Last week, technology analysis firm CB Insights published an update on the status of its list of top 100 AI startups of 2020 (in case you don't know, CB Insight publishes a list of 100 most promising AI startups every year). Out of the hundred startups, four have made exits, with three going public and one being acquired by Facebook.

Expectations for customer service are higher today than a year ago, with the coronavirus pandemic fueling online shopping and challenging enterprise customer service operations, according to Customer Thermometer. That puts companies offering automation solutions in the right place at the right time. Directly of San Francisco, cofounded by Antony Brydon, Jean Tessier and Jeff Patterson, offers a platform to integrate into call centers and provide a mix of automation and human support. Directly recently added $11 million in funding to bring its total investor commitment to $66.8 million, according to Crunchbase. The Directly platform is trained by thousands of subject matter experts to analyze call center interactions and provide a degree of automation, according to a recent account in VentureBeat.

Suki, a startup that makes an AI-powered voice assistant for doctors, received two pieces of news from major clients less than three weeks ago. The first was that they'd need the product to accommodate telemedicine visits. The second was that they'd need autofilled clinical notes to quickly process patients who test positive for COVID-19 -- "Hey, Suki, write up a completed clinical note for COVID-19." The clients sent Suki their requirements and the data they'd gathered so far, and CEO Punit Soni called a meeting with his COO, head of customer success, product lead, sales lead and marketing lead. The meeting lasted 20 minutes.

Butterfly Network is reinventing medical imaging and championing a new era of healthcare by creating the first ever pocket-sized, whole-body ultrasound device – the Butterfly iQ. This breakthrough technology has reduced the cost of the traditional ultrasound system by miniaturizing it onto a single semiconductor silicon chip. Our mission is to democratize healthcare by making medical imaging accessible to everyone around the world. Since inception, Butterfly has raised over $375 million. The iQ is FDA-cleared and is being sold in hospitals and clinics around the globe.

Dr. Zaius: I think you're crazy. The concept of a second opinion in medicine is so common that most people take it for granted, especially given a severe diagnosis. Disagreement between two doctors may be due to different levels of expertise, different levels of access to patient information or simply human error. Like all humans, even the world's best doctors make mistakes. At Butterfly, we're building machine learning tools that will act as a second pair of eyes for a doctor and even automate part of their workflow that is laborious or error prone.

Which products haven't been imbued with artificial intelligence (AI) at this point? In 2018, analysts pegged the global AI market at a whopping $7.35 billion, buoyed by the influx of machine learning-aided image recognition, object identification, detection, and classification, and geophysical detection startups, apps, and services in every conceivable sector (and particularly enterprise). But as with all promising technologies, not every AI startup, app, and service will pan -- or has panned -- out, and it becomes harder with each passing day to separate the wheat from the chaff. Fortunately, the folks at CB Insights have taken the bull by the horns. They today published their third annual cohort of AI startups -- a compilation of 100 of the most promising companies (whittled down from a pool of over 3,000) providing hardware and data infrastructure for AI apps, optimizing machine learning workflows, and applying AI across a range of industries.

If Jonathan Rothberg has a superpower, it's cramming million-dollar, mainframe-sized machines onto single semiconductor circuit boards. The entrepreneurial engineer got famous (and rich) inventing the world's first DNA sequencer on a chip. And he's spent the last eight years sinking that expertise (and sizeable startup capital) into a new venture: making your smartphone screen a window into the human body. Last month, Rothberg's startup Butterfly Network unveiled the iQ, a cheap, handheld ultrasound tool that plugs right into an iPhone's lightning jack. You don't have to be a technician to use one--its machine learning algorithms guide the user to find what they might be looking for.