Well-known Belfast startup Axial3D produces 3D prints of your body parts. This isn't to satisfy the narcissistic social media types – it has important surgical implications. This previous TechWatch article describes the company's process. Now, Axial3D is developing new AI techniques to make instantaneous the transition from 2D images to 3D prints. How are they doing that?

My sandwich looked like something out of a restaurant commercial: glossy yellow cheese oozing between two golden-brown slices of toast. The first bite should have been rich, gooey and decadent. I stalked back to the kitchen, pulled open the fridge door, and snatched the culprit. Printed on the back of the plastic package--Mexican Style Blend Finely Shredded Cheese, left by a visiting friend--was an ingredient list longer than just "cheese." Food additives are a fact of modern life--they improve shelf-life, flavor, texture, consistency and color.

Artificial intelligence (AI) is emerging as a key tool in just about every industry, from marketing to recruitment and beyond. But one particularly powerful application for AI is in health care, where we're already seeing early signs of its potential. Iowa-based Idx is one startup using AI to detect early signs of specific medical conditions. Its first system, IDx-DR, is an AI diagnostic system that analyzes images of the retina for signs of diabetic retinopathy, a complication of diabetes caused by high sugar levels. This means that health care providers, including doctors who are not eye care specialists, can use the IDx-DR system to detect diabetic retinopathy without needing to bring in a specialist clinician to interpret the image scan or results.

It says the irregular rhythm detector isn't for people who've been diagnosed with atrial fibrillation. And both the EKG and heart rhythm function are "not intended to replace traditional methods of diagnosis or treatment." As The Washington Post has reported, some cardiologists worry people taking Watch EKGs could result in a flood of unnecessary office visits by healthy people. The heart sensors can let people with heart conditions or anxiety know when they might need to take it easy. Margery Widroe, 80, who's been using a Series 3 Apple Watch for a few months, recounted to our group a recent incident when she was at the grocery store and her Watch alerted her to a high heart rate.

AI will change radiology, but it won't replace radiologists.

In the early days of computing, there was an acronym: GIGO. It stands for Garbage In, Garbage Out. The few people in the mainframe industry understood that if the data going into the system wasn't good then than what came out wasn't accurate information. The advent of the PC meant far more people began using computers, and most of them understood far less than the early programmers and users. A pundit pointed out the GIGO began to mean Garbage In, Gospel Out.

With the rapid development of high-throughput technologies, parallel acquisition of large-scale drug-informatics data provides huge opportunities to improve pharmaceutical research and development. One significant application is the purpose prediction of small molecule compounds, aiming to specify therapeutic properties of extensive purpose-unknown compounds and to repurpose novel therapeutic properties of FDA-approved drugs. Such problem is very challenging since compound attributes contain heterogeneous data with various feature patterns such as drug fingerprint, drug physicochemical property, drug perturbation gene expression. Moreover, there is complex nonlinear dependency among heterogeneous data. In this paper, we propose a novel domain-adversarial multi-task framework for integrating shared knowledge from multiple domains. The framework utilizes the adversarial strategy to effectively learn target representations and models their nonlinear dependency. Experiments on two real-world datasets illustrate that the performance of our approach obtains an obvious improvement over competitive baselines. The novel therapeutic properties of purpose-unknown compounds we predicted are mostly reported or brought to the clinics. Furthermore, our framework can integrate various attributes beyond the three domains examined here and can be applied in the industry for screening the purpose of huge amounts of as yet unidentified compounds. Source codes of this paper are available on Github.

Developing a new drug can cost billions of dollars and take a dozen or more years to bring to market. Two Israeli researchers have applied artificial intelligence (AI) and deep learning to shave time and money off the drug-discovery process. Instead of searching for the appropriate molecules to use in a new medicine, as is done today, they enabled a computer to make smart predictions without human guidance. Shahar Harel and Kira Radinsky at the Technion-Israel Institute of Technology fed into their computer system hundreds of thousands of known molecules as well as the chemical composition of all FDA-approved drugs up until 1950. Aided by AI, the computer came up with new potential molecules by making sometimes unexpected correlations from within this massive sample.

These are perhaps the most powerful and important four words a woman can hear after a breast-screening visit. X-ray based mammography is an effective screening tool for detecting cancer, but what many women may not know is that breast screening programs produce a high level of false positive results, particularly after multiple years of screening. In other words, women are informed they may have cancer when in fact they don't. This is particularly true in the U.S., where each study is generally read by a single, expert radiologist. In Europe, two independent radiologists read each study.

These are perhaps the most powerful and important four words a woman can hear after a breast-screening visit. X-ray based mammography is an effective screening tool for detecting cancer, but what many women may not know is that breast screening programs produce a high level of false positive results, particularly after multiple years of screening. In other words, women are informed they may have cancer when in fact they don't. This is particularly true in the US, where each study is generally read by a single, expert radiologist. In Europe, two independent radiologists read each study.