Berg Health's cofounder and chief executive Niven R. Narain is used to being laughed out of rooms, given his interest in bringing artificial intelligence into the drug development process. But things have changed, he says, thanks to growing excitement around the practice known as precision medicine. This afternoon, at the Fast Company Innovation Festival, Narain spoke on a panel on the topic of bringing advanced technologies to medicine with industry experts from Mount Sinai and Columbia University. Precision medicine is an all-encompassing term, which broadly refers to the idea of treating patients in a more personalized, targeted way rather than taking a one-size-fits-all approach to disease. The White House announced a $215 million investment in precision medicine earlier this year; if nothing else, it generated a lot of hype.

Introduction: In the first blog, we decided on the predictors. We knew that different predictive models have different assumptions about their predictors. Random Forest has none, but Logistic Regression requires normality of the continuous variables, and assumes the probability between 2 consecutive unit levels in a series of numbers to stay constant. K Nearest Neighbors requires the predictors to be at least on the same scale. SVM, Logistic Regression, and Neural Networks tend to be sensitive to outliers.

Mark Zuckerberg and his wife, Priscilla Chan, recently announced a $3 billion effort to cure all disease during the lifetime of their daughter, Max. Earlier this year, Silicon Valley billionaire Sean Parker donated $250 million to increase collaboration amongst researchers to develop immune therapies for cancer. Google is developing contact lenses for diabetic glucose monitoring; gathering genetic data to create a picture of what a healthy human should be; and working to increase human longevity. The technology industry has entered the field of medicine and aims to eliminate disease itself. It may well succeed because of a convergence of exponentially advancing technologies such as computing, artificial intelligence, sensors, and genomic sequencing.

AliveCor, the leader in FDA-cleared mobile electrocardiogram (ECG) technology for mobile devices, announced a collaboration with Mayo Clinic to utilize AliveCor's unique measurement technology to unlock previously hidden health indicators in ECG readings. These indicators have the potential to not only improve heart health but also overall health care for a variety of conditions. AliveCor provides the first consumer-ready, clinically validated and FDA-cleared ECG to give patients a more complete view of their heart health, improve proactive monitoring and create a new standard of cardiac care. By using AliveCor's deep machine learning capabilities applied to 10 million of its user ECG recordings, Mayo Clinic and AliveCor will work together to uncover hidden physiological signals to improve heart and overall human health. "Mayo Clinic has pioneered new approaches that may uncover significant measures of physiology that have been hidden in individuals' ECGs," said Vic Gundotra, CEO, AliveCor.

Launched by digital health and weight-loss platform Lose It!, the new feature of an already existing app proposes a simple solution to those who struggle to keep track of their caloric intake: Take a photo of your food, and Snap It will immediately display its calorie count Showing people the caloric value of their foods before they eat them can help modify their eating habits. Some studies have shown that keeping a food journal helps people stick to their diet. And in an effort to fight rising obesity rates, the FDA announced in 2014 that chain restaurants throughout the U.S. will have to post calorie information in their menus (the rule is set to go into effect sometime next year). But the FDA rules won't apply to all restaurants. And food diets are cumbersome, tending to go the way of new year's resolutions.

I'd be willing to bet that if you were asked to list ten uses for your smartphone, you probably wouldn't include "medical device" in your answer. But as smartphones become increasingly capable, highly-portable computing platforms, researchers are looking to the computer in everyone's pocket as a way to improve global health. As Wired UK declared earlier this year, the next revolutionary medical device is likely to be your smartphone. Scientists have already developed smartphone-based apps that can monitor asthma, detect skin cancer, and diagnose traumatic brain injuries. The latest app that joins the "doctor in your pocket" list is helping screen for anemia.

When Kevin Oldt crashed his snowmobile in January 2001, his doctors told him that his spinal cord injury meant he'd never walk again. So it's with great satisfaction that he tells me his personal record for walking with the help of a robotic exoskeleton from Ekso Bionics: about 5000 steps in one day. He clocked those steps in a Dallas convention hall where he spent four hours on the show floor demonstrating the features of the Ekso GT, the company's current commercial model. "And I didn't feel tired," he says. Oldt recently gave IEEE Spectrum a personal demo at a New York City hotel [video below], accompanied by Ekso CEO Tom Looby and an Ekso physical therapist.

One of the many questions in the design and use of Clinical Decision Support software is whether or not the user can recreate the logic used by the system in reaching its conclusions and recommendations–or alerts, or suggestions. If the CDS is based on sound medical logic, perhaps supported by specific reference material, then the user could in principle reach the same conclusions by reading the same literature, or perhaps reach a different conclusion. This transparency was part of the proposed criteria for some CDS systems not falling under FDA regulation in 2015 federal draft legislation--which didn't pass. The FDA has otherwise not been forthcoming on the general subject of CDS despite many pleas for guidance, and a draft guidance in this domain is an as yet unfulfilled part of the 2015 strategic plan. However underlying logic and science is not the only way to build "artificial intelligence" (AI), which might in some instances turn out to be artificial mediocrity if not artificial stupidity.

If you trade stocks, you've probably made a few losing trades that still keep you up at night. The stock market moves quickly, and volatility can lead even the most seasoned trader to buy or sell in a panic. Whatever strategy you choose, the most important thing for your long-term returns is that you stick to it. You'd be better off if you stopped yourself before trading in a panic. Unfortunately, human emotions are powerful, and it's hard to think rationally when your nervous system has other plans.

New drugs typically take 12-14 years to make it to market, with a 2014 report finding that the average cost of getting a new drug to market had ballooned to a whopping 2.6 billion. It's a topic I've covered before, with a study published earlier this year highlighting how automation could be used to reduce the cost of drug discovery by approximately 70%. It's an approach that a number of companies are taking to market. For instance, London based start-up Benevolent.AI utilizes complex AI to look for patterns in the scientific literature. They have already managed to identify two potential drug targets for Alzheimer's that has already attracted the attention of pharmaceutical companies.