When I joined MD DI nearly a year ago, I would hear smatterings about artificial intelligence and machine learning in medtech. By mid-February every other pitch I started receiving was AI-related. Conferences started delving into the AI topic. I even participated in a webinar hosted by Dave Saunders, CTO and Co-founder of Galen Robotics. It was clear to me that AI wasn't just a buzzword, or some fancy marketing scribble used to catch a journalist's attention.

Amid the country's growing substance abuse crisis, last year the FDA cleared reSET, a mobile app that tracks substance use, cravings, and social triggers to treat dependency on alcohol, cocaine, and cannabis. The FDA's clearance makes reSET one of the first prescription "digital therapeutics"--an emerging class of evidence-based interventions that are predominantly driven by software rather than drugs. Andy Coravos is is the CEO of Elektra Labs and a member of the Harvard-MIT Center for Regulatory Science. Earlier this year, digital medicine company Akili Interactive announced that its video game for children with ADHD demonstrated a statistically significant improvement in a randomized, controlled clinical trial. That milestone paves the way for what could be the first prescription video game.

We're excited to share an interview with Dr. Mark Traill, who is the owner of MammoBot LLC. Mammobot is a cloud-based startup that offers a double reading of screening 2D or 3D mammograms using artificial intelligence algorithms. Clients can upload mammogram studies that become part of the investigational protocol prior to pending FDA approval of the algorithm. Review of the mammogram study by AI algorithms has the potential of detecting early breast cancer that was missed on the initial human review of images. Ambra Health: When did Mammobot begin?

Every day sees strides across the field of artificial intelligence, and healthcare is just one of the many industries looking to smart automation as a means to reduce burden and improve results. The last year in particular has brought a wealth of new healthcare focused software tools to the forefront, and as such has ignited debate on how these algorithms are being reviewed and regulated by the FDA. "FDA is lagging in the production of guidance to explain its approach for these newer products. This is a problem, because Commissioner Gottlieb himself in a blog post noted well over a year ago that individual decision-making by FDA is not enough for digital therapeutics to thrive," Bradley Merrill Thompson, a lawyer at Epstein Becker & Green who also leads CDS Coalition, an industry group, wrote in an email on the subject. "Industry has been asking since 2015 for better guidance on the use of software-based algorithms in connection with drug administration. The commissioner, starting in April 2018, has been promising new guidance focused on the use of software with drugs, and in fact reiterated that promise only a couple weeks ago. But the concern is that the new guidance may not be focused on the issues of greatest concern to industry. We shall have to wait to see." Thompson also noted that while the agency is relying on its 510(k) regulatory pathways in the meantime, the heterogeneity of these nontraditional tools has resulted in an ever-growing number of De Novo clearances and device classifications.

Adverse drug reactions or adverse drug events refer to unwanted or harmful reactions experienced following the administration of a medicine or combination of medicines under normal conditions of use. A clinician or patient then suspects the reaction, such as rash or a headache, to be linked to the drug. The event is reported back to the manufacturer of the medicine for investigation and be subject to scrutiny by a regulatory agency, such as the U.S. Food and Drug Administration (FDA). The practice of monitoring the effects of medical drugs after they have been licensed for use, especially in order to identify and evaluate previously unreported adverse reactions is referred to as pharmacovigilance, and monitoring is an activity incumbent upon drug manufacturers. The process is also designed to support public health programs by providing reliable, balanced information for the effective assessment of the risk-benefit profile of medicines.

It's never been quite clear, she says, whether the phrase--which is frequently the entire output of a student's first computer program--is supposed to be attributed to the program, awakening for the first time, or to the programmer, announcing their triumphant first creation. Perhaps for this reason, "Hello World" calls to mind a dialogue between human and machine, one which has never been more relevant than it is today. Her book, called Hello World, published in September, walks us through a rapidly computerizing world. Fry is both optimistic and excited--along with her Ph.D. students at the University of College, London, she has worked on many algorithms herself--and cautious. In conversation and in her book, she issues a call to arms: We need to make algorithms transparent, regulated, and forgiving of the flawed creatures that converse with them.

The Mayo Clinic and health technology vendor Eko are working together to develop and commercialize a machine learning-based algorithm that screens patients for low ejection fraction, which is linked to heart failure. A low ejection fraction number, often measured by an echocardiogram, suggests problems with the heart's pumping function. However, echocardiography is an expensive and time-consuming medical imaging test using ultrasound that is less accessible than a doctor with a stethoscope. "With this collaboration, we hope to transform the stethoscope in the pocket of every physician and nurse from a hand tool to a power tool," said Paul Friedman, MD, chair of cardiovascular medicine at the Mayo Clinic. "The community practitioner performing high school sports physicals and the surgeon about to operate may be able to seamlessly tap the knowledge of an experienced cardiologist to determine if a weak heart pump is present simply by putting a stethoscope on a person's chest for a few seconds."

From my Northern Irish vantage point, I coordinate and facilitate a collaborative network around AI. Our 30 members range from micro SMEs through to multinational organisations, such as Liberty IT and Allstate. As such, I am privy to an incredible range of AI based applications and solutions that are coming down the line, and I am always surprised at the pace of change in the industry. With every change, we need to take a few steps back and rethink how to frame the state of the art at that given time – so it's worth keeping in mind that what is state of the art at any given time may well be seen as mundane in just a few short months. For example, when the age of the AI personal assistant arrived with Google Assistant, Siri and Cortana, my framing focussed on trying to communicate that AI was no longer an abstract concept, but part of our everyday lives; albeit in a relatively limited manner. But it was only with the arrival of Amazon Alexa that many people were spending real money to own what is ultimately an AI product. Well… today things feel different again.

No matter what industry you're in, Artificial Intelligence (AI) is all the rage. In pop culture alone it's the central theme of HBO's Westworld, where humanoid AI robots pretend to be people, or even the most recent season of Silicon Valley where a major character was an AI-powered robot named Fiona. AI is also the central, recurring theme at every conference. Even at giant tradeshows like the Consumer Electronics Show (CES) where, this year, we saw autonomous vehicles, voice-enabled bot driving assistants within cars, L'Oreal's thumbnail-sized UV sensor patch, and hundreds of other AI-enabled "smart" products. At South by Southwest (SXSW), it seemed every other session was about AI.

Algorithms based on machine learning and deep learning, intended for use in diagnostic imaging, are moving into the commercial pipeline. However, providers will have to overcome multiple challenges to incorporate these tools into daily clinical workflows in radiology. There now are numerous algorithms in various stages of development and in the FDA approval process, and experts believe that there could eventually be hundreds or even thousands of AI-based apps to improve the quality and efficiency of radiology. The emerging applications based on machine learning and deep learning primarily involve algorithms to automate such processes in radiology as detecting abnormal structures in images, such as cancerous lesions and nodules. The technology can be used on a variety of modalities, such as CT scans and X-rays.