Scientists, researchers, and innovators come up with amazing breakthroughs every year, and that was no different in 2017 either. No matter whether we look at physics (proving the existence of gravitational waves), astronomy (discovering new planets) or biology (detecting a fluorescent frog in Brazil), we come across mind-blowing scientific findings everywhere. Healthcare was no exception, moreover, some say the field of medicine is one of the most innovative fields today. Looking back at our expectations in digital health for 2017 in terms of trends, innovations or breakthroughs, there were at least three developments and three trends, which proved to be foresightful. The beginning of a new era in diabetes care is upon us with the realities of the FDA-approved artificial pancreas or such small, but significant innovations as a glucose monitor built into phone cases.

Designing new and efficient therapies is a long and ever more costly process, with less than ten percent of new treatments entering Phase I finally being approved by the FDA and commercialized [1, 2]. One of the major challenges for the improvement of drug development is to better understand how drugs interact with patients, particularly for treatments displaying heterogeneous responses. Therefore, conducting a detailed analysis of clinical trial data is critical to find subgroups of patients with higher benefit-risk ratio or to understand why a drug does not work on some subpopulation to improve existing therapeutic strategies. Moreover, understanding the relationships of patient descriptors which compose the most responsive cross-section of the population is of great importance when planning a Phase III trial, for salvaging failed trials, or accelerating advances in personalized medicine. This process of biomarker identification is critical to detect subgroups within a given indication, but, as shown recently for immunotherapies, can also provide the basis for pan-indication drug approval [3].

The FDA has been championing digital health of late with wide-ranging guidance that derives from the 21st Century Cures Act. This legislation acknowledges the potential that digital health has to make a difference in patient care, potentially leading to more precise therapies. Several developments this week show that the regulator is right to be excited about its potential. Some of the most exciting advances have come in the field of cancer – medical devices firm Angle has produced a new analysis showing that its liquid biopsy device Parsortix could be used instead of conventional tissue biopsies. Parsortix works by monitoring a patient's bloodstream for circulating cancer cells and the University of Southern California research adds to the body of evidence showing that liquid biopsies could replace invasive and unpleasant tissue biopsies in the future.

The device, made by AliveCor, pairs the ability to take a personal 30 second electrocardiogram (EKG) with a feature that uses artificial intelligence to continuously evaluate the correlation between heart and physical activity. When the device, known as KardiaBand, detects that a user's heart rate and activity are out of sync, it prompts the user to capture an EKG by touching the band. The results display instantly on the watch face. The device is designed to capture information that can help doctors help manage atrial fibrillation, the most common heart arrhythmia that is a leading cause of stroke and affects more than 30 million people worldwide. The device costs $199 and requires a subscription to the company's premium service for $99 a year.

AliveCor's KardiaBand, a device that detect dangerous heart rhythms, has become the first Apple Watch accessory cleared for medical use by the FDA, the company announced. It can capture your EKG in 30 seconds, then detect problems like atrial fibrillation, a type of heart arrhythmia. In addition, the company launched a new version of the band today in the US with a feature called SmartRhythm. That uses Apple's built-in heart rate sensor and AI algorithms to warn you if your heart rate is elevated when you're not exercising or doing strenuous activities. FDA clearance means that AliveCor can sell and market it as a medical device, but users don't need doctor approval to use it.

Kardia Band attaches to the Apple Watch like any other replaceable watch band. The user rests a finger on the sensor pad embedded in the band, allowing an EKG reading to be taken. The Kardia Band transmits its EKG reading to the Apple Watch (via a high-pitch audio signal) where it's displayed in real time as a moving waveform. When the 30-second EKG is finished, the user can view it on their phone or easily send the results as a PDF to their physician. Doctors can diagnose hundreds of diseases using EKG data, but AliveCor is cleared by the FDA only to record the EKG and to advise a user that the reading is normal, or possibly indicative of atrial fibrillation.

Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of-the-art models without hand labeling any training data. Instead, users write labeling functions that express arbitrary heuristics, which can have unknown accuracies and correlations. Snorkel denoises their outputs without access to ground truth by incorporating the first end-to-end implementation of our recently proposed machine learning paradigm, data programming. We present a flexible interface layer for writing labeling functions based on our experience over the past year collaborating with companies, agencies, and research labs. In a user study, subject matter experts build models 2.8x faster and increase predictive performance an average 45.5% versus seven hours of hand labeling. We study the modeling tradeoffs in this new setting and propose an optimizer for automating tradeoff decisions that gives up to 1.8x speedup per pipeline execution. In two collaborations, with the U.S. Department of Veterans Affairs and the U.S. Food and Drug Administration, and on four open-source text and image data sets representative of other deployments, Snorkel provides 132% average improvements to predictive performance over prior heuristic approaches and comes within an average 3.60% of the predictive performance of large hand-curated training sets.

When I was a scrawny little chap, shortest in my high school class, I always wanted a super power. Wanted doesn't capture the feeling. I would have given a limb for a super power. I read a lot of books back then (and now) and landed on a super power that had something to do with the brain. I eventually landed on Prof. Xavier of the X-Men.

Investigators from Brigham and Women's Hospital and Dana-Farber Cancer Institute are leveraging the power of artificial intelligence to develop a new technique to detect ovarian cancer early and accurately. The team has identified a network of circulating microRNAs - small, non-coding pieces of genetic material - that are associated with risk of ovarian cancer and can be detected from a blood sample. Their findings are published online in eLife. Most women are diagnosed with ovarian cancer when the disease is at an advanced stage, at which point only about a quarter of patients will survive for at least five years. But for women whose cancer is serendipitously picked up at an early stage, survival rates are much higher.

In 1983, the IBM PC XT debuted with 128K of RAM and a 10MB hard disk. In that same year, the first mobile phone debuted weighing about 2.5 pounds and with a $4,000 price tag. Fast forward to today and the average person unlocks their smartphone 76-80 times a day and relies on it for every aspect of their lives. These amazing pieces of hardware are millions of times more capable than all of NASA's computing power in the 1960s. Now that we have a supercomputer that never leaves people's sides, maybe it's time that we do some more innovation and see how that device can be used for "mobile health".