Cardio-focused digital health company AliveCor landed FDA clearance for its new suite of interpretive ECG algorithms, dubbed the Kardia AI V2. This news comes just days after the company announced a $65 million Series E funding round. The new clearance will is able to capture sinus rhythm with premature ventricular contractions, sinus rhythm with supraventricular ectopy and a sinus rhythm with wide QRS. The algorithm works on AliveCor's KardiaMobile and KardiaMobile 6L devices, which even before this latest FDA clearance, have been able to take 30-second ECGs, and are hooked up to a corresponding app. According to the company's release, the algorithm will also reduce the number of unclassified readings, and has improved sensitivity and specificity on the company's normal and atrial fibrillation algorithms. Users will also have new visualization tools that let them see heart beat average, PVC identification and tachogram.

Technology has helped cardiologist Eric Topol save lives. While on an airplane several years ago, a flight attendant asked if there was a doctor on board--a man was suffering from chest pain at 30,000 feet. Topol was able to obtain an electrocardiogram from the man by using a heart activity–reading gadget that attached to his smartphone, made by the medical device company AliveCor. "It turned out to be a big anterior heart attack I could see right on my smartphone," says Topol, director and founder of the Scripps Research Translational Institute in San Diego. "I had to tell the folks to land the plane. He wound up doing pretty well."

Over the last couple of months, we've explored how AI is or will be applied in a wide range of medical markets, from ophthalmology to dentistry to Wound Care and some have started to realise the potential of AI whilst others are still just working it out. In cardiology however, there is a problem which AI can, and will, help with immediately – Atrial Fibrillation (AF). AF is, essentially, an irregular heartbeat which occurs in sporadic'episodes' and is estimated to affect tens of millions of people around the world. On their own these episodes may not cause immediate damage, but they can be indicative of a more serious problem; the condition is one of the leading causes of strokes and comes with an increased risk of heart failure and dementia. The problem with these episodes is that a patient doesn't have access to their cardiologist during the event.

Artificial Intelligence is a new technology and, for the most part, one of its biggest challenges is just working out how it will be applied. Where it can be used best, most cost-effectively and easiest to gain the best results. Over the last couple of months, we've explored how AI is or will be applied in a wide range of medical markets, from ophthalmology to dentistry to Wound Care and some have started to realise the potential of AI whilst others are still just working it out. In cardiology however, there is a problem which AI can, and will, help with immediately – Atrial Fibrillation (AF). AF is, essentially, an irregular heartbeat which occurs in sporadic'episodes' and is estimated to affect tens of millions of people around the world.

Many doctors take a very conservative view when it comes to new technology. But Eric Topol, a cardiologist and author, is a self-described "digital geek," a long-time proponent of using the latest gadgets in medicine. He even upgraded his black bag to include wearables and connected medical devices, rather than the old analog blood pressure cuffs and stethoscopes. So for technology companies like Apple, Alphabet and Microsoft, Topol is an important ally as they get deeper into the medical sector. It can't hurt that he's one of the most influential doctors on social media, with over 150,000 Twitter followers.

It begins seven years ago, when my doctor asks me whether I want to lose my foot. I say to him: No, I do not want to lose my foot. "Good," he says back: Monitor your blood sugar, keep it down, and we can manage this disease. Then nobody has to lose a foot. It turns out I have type 2 diabetes, which--from a patient's point of view--boils down to a single data point: the amount of glucose in my bloodstream.

Medtech startup AliveCor announced this morning it has pulled in $30 million from Omron Healthcare and the Mayo Clinic and is launching an artificially intelligent stroke prevention platform for doctors called KardiaPro. AliveCor already has an FDA-cleared mobile app called Kardia to accompany its $99 standalone EKG reader device. However, a partnership last year with the Mayo Clinic involving 4,500 patients for a major study on stroke prompted the company to build the new platform, which is a premium offering for doctors who want to monitor the EKG readouts of patients at potential risk for stroke or other heart-related diseases. KardiaPro will track a number of factors for at-risk patients, including weight, activity and blood pressure and then runs them through the AliveCor AI technology to suss out potential triggers doctors may not detect on their own. The platform then feeds what AliveCor CEO Vic Gundotra refers to as a "personal heart profile" for patients that can then be used to send alerts to the doctor to help them determine the next course of action. AliveCor raised $13.5 million previously from Khosla Ventures, Qualcomm and Burrill and Company.

Machine-learning algorithms accomplish tasks by training on a set of data, rather than being programmed by humans. Armed with the knowledge of what worked before, the system instructs the implant to stimulate users' brains to interrupt a seizure at its onset. The innovation is part of a larger phenomenon that has big implications for how we identify and treat disease: the introduction of artificial intelligence to consumer and clinical electronics. As machines learn from at times millions of humans, doctors are gaining the ability to better identify disease and even predict it before it becomes catastrophic. As in every other area of human endeavor, the introduction of AI to medicine comes with challenges.

Machine-learning algorithms accomplish tasks by training on a set of data, rather than being programmed by humans. Armed with the knowledge of what worked before, the system instructs the implant to stimulate users' brains to interrupt a seizure at its onset. The innovation is part of a larger phenomenon that has big implications for how we identify and treat disease: the introduction of artificial intelligence to consumer and clinical electronics. As machines learn from at times millions of humans, doctors are gaining the ability to better identify disease and even predict it before it becomes catastrophic. As in every other area of human endeavor, the introduction of AI to medicine comes with challenges.

LQTS, which can be a congenital or acquired disorder, causes around 4,000 deaths in children and young adults annually. Identifying patients with LQTS is difficult due to some patients showing normal QTc on in their electrocardiograms (ECGs). In this study, researchers aimed to provide clinicians with a tool in diagnosing the disorder using AI and deep neural networks. "There can be no better illustration of the importance of our AI to medical science than using it to detect that which is otherwise invisible," said Vic Gundotra, CEO of AliveCor. By applying AI to data from lead I of a 12-lead ECG, researchers were able to use machine learning to achieve a specificity of 81 percent, sensitivity of 73 percent and an overall accuracy of 79 percent in identifying LQTS patients.