Radhika previously worked in content marketing at three technology firms, and graduated from Sri Krishna College Of Engineering And Technology with a degree in Information Technology. According to the National Institute of Mental Health, the United States is currently battling a mental health epidemic. One in every five Americans struggles with mental illness in one form or another. According to the Center for Workplace Mental Health founded by the American Psychiatric Association, up to 7% of full-time workers in the U.S. suffer from major depressive disorder, the economic cost of which is estimated to be $210.5 billion per year. When compared to other developed nations, traditional healthcare in the U.S. is notoriously costly; mental healthcare, even more so.

In the elegant quiet of the café at the Church of Sweden, a narrow Gothic-style building in Midtown Manhattan, Daniel Cohen is taking a break from explaining genetics. He moves toward the creaky piano positioned near the front door, sits down, and plays a flowing, flawless rendition of "Over the Rainbow." If human biology is the scientific equivalent of a complicated score, Cohen has learned how to navigate it like a virtuoso. Cohen was the driving force behind Généthon, the French laboratory that in December 1993 produced the first-ever "map" of the human genome. He essentially introduced Big Data and automation to the study of genomics, as he and his team demonstrated for the first time that it was possible to use super-fast computing to speed up the processing of DNA samples.

The US Food and Drug Administration has proposed a framework on how it might regulate medical devices that rely on AI and machine learning algorithms. The report published this week outlines two types of algorithms for the purposes of regulation: "locked algorithms" and "adaptive algorithms." Locked algorithms provide the same result each time they're fed the same input. The answers are normally based on things like look-up tables, decision trees, or classifiers. An adaptive algorithm, however, will "change its behavior using a defined learning process."

The US Food and Drug Administration has announced that it is preparing to regulate AI systems that can update and improve themselves as they gorge on more training data. The announcement: The agency released a white paper proposing a regulatory framework to decide how medical products that use AI should seek approval before they can go on the market. It is the biggest step the FDA has taken to date toward formalizing oversight of products that use machine learning (ML). The challenge: Machine-learning systems are tricky to regulate because they can continuously update and improve their performance through new training data. In instances where the FDA has approved ML-based medical software before, it has required the algorithms to be "frozen" before commercial deployment and to go through a reapproval process when they are changed.

The field of image reconstruction has undergone four waves of methods. The first wave was analytical methods, such as filtered back-projection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on simple mathematical models for the imaging systems. These methods are typically fast, but have suboptimal properties such as poor resolution-noise trade-off for CT. The second wave was iterative reconstruction methods based on more complete models for the imaging system physics and, where appropriate, models for the sensor statistics. These iterative methods improved image quality by reducing noise and artifacts. The FDA-approved methods among these have been based on relatively simple regularization models. The third wave of methods has been designed to accommodate modified data acquisition methods, such as reduced sampling in MRI and CT to reduce scan time or radiation dose. These methods typically involve mathematical image models involving assumptions such as sparsity or low-rank. The fourth wave of methods replaces mathematically designed models of signals and processes with data-driven or adaptive models inspired by the field of machine learning. This paper reviews the progress in image reconstruction methods with focus on the two most recent trends: methods based on sparsity or low-rank models, and data-driven methods based on machine learning techniques.

FDA Commissioner Scott Gottlieb is making the most of his final week at the agency. In the month that has passed since Gottlieb rattled the medical device industry with news of his impending resignation, the commissioner has issued 18 public statements pertaining to nearly all corners of the agency's realm, from food, tobacco, and cosmetics to drugs and devices. Friday is Gottlieb's last day on the job. On Tuesday, Gottlieb said the agency will consider a new regulatory framework for reviewing medical devices that use advanced artificial intelligence algorithms. AI has been making headlines in medtech for a while now, and this is certainly not the first time FDA has turned its attention to how AI-based medical devices should be regulated.

Please click the subscribe button at the top of this article to have articles in our DeepTech series delivered directly to you each week. Researchers at Iowa State University designed an AI system to create personalized prosthetic aortic heart valves. These customized valves can restore normal blood flow for people with aortic valvular disease. Over 90,000 people in the US have valve replacement surgery every year. Traditional drug discovery is a very long and expensive process involving many tests to determine the safety and efficacy of each new drug candidate.

Artificial intelligence raises exciting possibilities for healthcare, but are companies promising more than they can deliver? But artificial intelligence's potential also comes with an incredible level of hype. "AI has the most transformative potential of anything I've seen in my life, and I graduated medical school 40 years ago. It's the biggest thing I've ever seen by far," prominent cardiologist and author Dr. Eric Topol told Medical Design & Outsourcing. "But it's more in promise than it is in reality."

The Stanford Institute for Human-Centered AI officially launched today. Stanford HAI seeks to become an interdisciplinary global AI hub and to fundamentally change the field of AI by integrating a wide range of disciplines and prioritizing true diversity of thought. Researchers in Korea analyzed literature evaluating 516 AI algorithms for medical image analysis and found that only 6% validated their AI and 0% were ready for clinical use. This lack of appropriate clinical validation is referred to as digital exceptionalism. An analysis of 47 biomedical unicorns found that most of the highest valued startups in healthcare have a limited or non‐existent participation in the publicly available scientific literature.

Zebra Medical Vision recently revealed that two of its imaging products had received CE certification in Europe, to expedite diagnosis and review of critical conditions, especially in medical imaging. The company's AI technology has the potential of flagging time-sensitive cases like brain bleed in CT scans and pneumothorax in chest X-rays. According to the company, the technology minimizes the duration taken by ER staff and radiologists in identifying critical conditions by 80 percent. As such, it helps in improving the timeliness and quality of treatment. The imaging analytics engine from Zebra not only links to any given medical picture archive system but also analyzes the necessary scans using its algorithms.