To see the new trends in radiology in 2018, just take a look back at 2017. AI has come a long way; wearables have become every day, 3D printing has matured, and IoT is, well, meh. Where is Artificial Intelligence (AI) in radiology today? This year, AI is gaining in respect and stature among radiology professionals. Last year in Everything Rad, we reported that AI inspired a mixture of wonder and fear among the radiology community.

FDA requires that all computer systems used to produce, manage and report on GxP (GMP, GLC, GCP) related products be validated and maintained in accordance with specific rules. This webinar will leave you with the information needed to plan, execute and document a computer system validation effort that meets FDA compliance standards. You'll learn about the various computer system validation deliverables and how to document them through the entire process. You will learn about what must be done to ensure the system remains in a validated state. In addition, you'll learn all about how to create and maintain good FDA-compliant documentation using a strategic approach based on the System Development Life Cycle (SDLC) Methodology.

The use of Artificial Intelligence (AI) is on the rise in the technology sector and has become a buzz-worthy topic in many corners of our digital world. The application of AI in the medical field holds great promise for improving patient health, but will doctors and patients feel comfortable using it? Young startups have begun leveraging this technology to prove better health outcomes, but there's still a lot to do before we'll see AI used pervasively in the clinic. To date, the sweet spot in healthcare AI has been pairing algorithms with structured exercises in reading patient data and medical images to train machines to detect abnormalities. This training is called "deep learning." In the same way, algorithms are being used to sift through vast amounts of medical literature to inform treatment decisions where it would be too onerous a task to have a human read through the same journals.

The Food and Drug Administration has approved Arterys' web-based imaging analytics platform, which marries cloud-based supercomputing and artificial intelligence, for clinical use. The FDA approval allows the Arterys product, called MICA, to supply clinical guidance to clinicians treating cardiac patients. The company is awaiting FDA clearance that would allow the platform to be used by oncology teams treating patients with lung and liver conditions. MICA is web-based and runs on a scalable distributed graphics processing architecture, enabling the product to apply various AI algorithms to image studies of specific cases. This addresses one of the major challenges of applying a variety of algorithms that use artificial intelligence to imaging studies--intelligently calling upon the right algorithm to provide guidance to clinicians while they are investigating a case or making treatment decisions.

MIT Technology Review spends most of the year identifying and writing about the most important emerging technologies. One day each year we highlight the worst of the lot. Some ideas just do not belong together. This year you can add "DIY--gene therapy" to the list. Josiah Zayner did it on video in August, injecting himself with a syringe full of the DNA-slashing chemicals known as CRISPR, in a blend he concocted himself to strengthen his muscles. Zayner, who operates an online shop for biohackers called (what else?)

The year 2017 is coming to a close, and as in years past, we look back with excitement at the medical technologies that have been gracing the pages of Medgadget. As usual, there are trends that have revealed themselves, with many research teams around the world working on similar technologies. There are also new devices that are unlike anything we've seen before, solving medical problems in novel and unexpected ways. Take a journey with us as we review the most innovative, full of impact, and revolutionary medical technologies of the past year! Ingestible devices, mostly in the form of cameras or other sensors that travel and assess the insides of the GI tract, have been around for a few years now.

The doctor enters and pulls up the electronic medical record. The patient's history is already there. The doctor drags and drops the image, presses the "analyze" button. An actionable diagnosis appears a moment later. If artificial intelligence (AI) were to one day take over much of clinical practice, as some fear or anticipate -- being potentially faster, more reliable, and generally better at certain tasks than humans -- clinical decisions may no longer depend on tired eyes, imperfect risk scores, or lagging guidelines.

Since Intuitive Surgical's da Vinci system earned the FDA nod in 2000, the company has enjoyed a sizable head start in the minimally invasive robotic surgery field. Competition has been brewing for years, from players large and small, but 2018 could be the year the market finally sees a shake-up. North Carolina's TransEnterix scored an FDA nod for its Senhance system in October, triggering a 75% bump in its stock price. The device, a rare new entrant to the robotic abdominal surgery market, is designed to make it easier to perform laparoscopic surgery. It is cleared for colorectal and gynecological surgery and features haptic feedback, so the surgeon can "feel" the tissue that the robotic arm is touching.

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].