Smartphone apps that use artificial intelligence to assess skin cancer risk based on images of suspicious moles aren't ready for prime time, a recent systematic review in the BMJ suggests. The 9 studies included in the review "showed variable and unreliable test accuracy" for 6 such apps, 2 of which are approved by European regulators as medical devices. Of those 2 apps, only 1 was supported by published peer-reviewed studies, and its accuracy in those studies was poor compared with experts. The reviewers concluded that, overall, the 9 diagnostic accuracy studies were small and of poor methodological quality. Among other problems, clinicians rather than consumers usually selected which moles were assessed and took the pictures.

"I would love to see a future where looking inside the body becomes as routine as a blood pressure cuff measurement," says Charles Cadieu '04, MEng '05. As president of the medical technology startup Caption Health, he sees that future in reach--with the help of artificial intelligence. Cadieu still remembers the "lightbulb moment" during his postdoctoral research at MIT when he realized that the field of AI would never be the same. He was working in the lab of James DiCarlo (now the Peter de Florez Professor of Neuroscience) on neural networks--AI systems made up of deep-learning algorithms that emulate the dense networks of neurons in the brain. Until then, neural networks had been unable to perform even simple visual tasks that the brain handles with ease.

US healthcare officials are working tirelessly to deliver coronavirus test results in a timely manner, but the process includes getting tested, having the sample processed and then delivering the results. Now, a scientist has developed new technology that can produce a diagnosis in just a matter of seconds and with 98 percent accuracy. Barath Narayanan, a scientist at the University of Dayton Research Institute, has designed a specific software code that can detect the disease just by scanning chest X-rays. The process uses a deep learning algorithm that was trained using scans of those with and without the disease in order to search searches for markings associated with coronavirus. A scientist has developed new technology that can produce a diagnosis in just a matter of seconds and with 98 percent accuracy.

Predicting whether a chemical structure shares a desired biological effect can have a significant impact for in-silico compound screening in early drug discovery. In this study, we developed a deep learning model where compound structures are represented as graphs and then linked to their biological footprint. To make this complex problem computationally tractable, compound differences were mapped to biological effect alterations using Siamese Graph Convolutional Neural Networks. The proposed model was able to learn new representations from chemical structures and identify structurally dissimilar compounds that affect similar biological processes with high precision. Additionally, by utilizing deep ensembles to estimate uncertainty, we were able to provide reliable and accurate predictions for chemical structures that are very different from the ones used during training. Finally, we present a novel inference approach, where the trained models are used to estimate the signaling pathways affected by a compound perturbation in a specific cell line, using only its chemical structure as input. As a use case, this approach was used to infer signaling pathways affected by FDA-approved anticancer drugs.

The multi-limbed da Vinci can be utilized in a variety of procedures, including cardiovascular, colorectal, gynaecological, head and neck, thoracic and urologic medical procedures, however, only if they're minimally invasive. How large the market could be is as yet hazy, yet experts concur the potential still can't seem to be tapped. So more players are moving in, and rapidly. As the beginning of robotic surgery offers an approach to increasingly precise control and better patient results, early pioneers like Intuitive Surgical Inc. are seeing increased pressure from large organizations like Johnson and Johnson and Medtronic PLC, which have made major M&A investments to break into the market as of late. Intuitive's da Vinci system was first affirmed by the U.S. Food and Drug Administration in 2000 for urology.

During the past decade, deep learning has seen groundbreaking developments in the field of AI (Artificial Intelligence). But what is this technology? And why is it so important? Well, let's first get a definition of deep learning. Here's how Kalyan Kumar, who is the Corporate Vice President & Chief Technology Officer of IT Services at HCL Technologies, describes it: "Have you ever wondered how our brain can recognize the face of a friend whom you had met years ago or can recognize the voice of your mother among so many other voices in a crowded marketplace or how our brain can learn, plan and execute complex day-to-day activities? The human brain has around 100 billion cells called neurons. These build massively parallel and distributed networks, through which we learn and carry out complex activities. Inspired from these biological neural networks, scientists started building artificial neural networks so that computers could eventually learn and exhibit intelligence like humans."

In response to the coronavirus health crisis, USC researchers have made a hard pivot, adapting labs and lessons learned from treating other diseases to help check the virus and save lives. At their disposal are numerous technologies that give a human advantage, despite the fast-break spread of COVID-19 once it exited central China and spread across the globe. The disease has afflicted thousands of Californians and poses a serious risk to public health and the world economy. Tools such as supercomputers, software apps, virtual reality, big data and algorithms are now in play. They are using the tools to find ways to search and destroy coronavirus DNA, turn smartphones into personal protection devices and use people-friendly simulators to help cope with the crush of medical cases.

We study the problem of estimating treatment effects when the outcome of primary interest (e.g., long-term health status) is only seldom observed but abundant surrogate observations (e.g., short-term health outcomes) are available. To investigate the role of surrogates in this setting, we derive the semiparametric efficiency lower bounds of average treatment effect (ATE) both with and without presence of surrogates, as well as several intermediary settings. These bounds characterize the best-possible precision of ATE estimation in each case, and their difference quantifies the efficiency gains from optimally leveraging the surrogates in terms of key problem characteristics when only limited outcome data are available. We show these results apply in two important regimes: when the number of surrogate observations is comparable to primary-outcome observations and when the former dominates the latter. Importantly, we take a missing-data approach that circumvents strong surrogate conditions which are commonly assumed in previous literature but almost always fail in practice. To show how to leverage the efficiency gains of surrogate observations, we propose ATE estimators and inferential methods based on flexible machine learning methods to estimate nuisance parameters that appear in the influence functions. We show our estimators enjoy efficiency and robustness guarantees under weak conditions.

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, with over 294,000 cases as of March 22, 2020 (WHO, 2020). In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis at different scales including molecular, medical and epidemiological applications. We finish with a discussion of promising future directions of research and the tools and resources needed to facilitate AI research. Executive Summary - There is a broad range of potential applications of AI covering medical and societal challenges created by the COVID-19 pandemic; however, few of them are currently mature enough to show operational impact.

AI tools and services are being used or offered by companies around the world to help fight the coronavirus pandemic. In a best-case scenario, whereby the virus transmission is massively mitigated, researchers from Imperial College London predict "there would still be in the order of 250,000 deaths in GB, and 1.1–1.2 million in the US" resulting from the coronavirus. Imperial College London's analysis landed in Washington over the weekend and it's said to be the reason behind the US stepping up its response. British PM Johnson warned that further measures in the UK will likely be introduced in the coming days and a coronavirus bill for emergency powers is making its way to the House of Commons. Much like in wartime, technologies and social experiments that under normal circumstances would take years or decades to be tested and implemented will be rushed into use in days or weeks.