Combining high-performance computing, high-fidelity modeling, and high-resolution medical imaging, produces simulations that have the potential to contribute to new diagnostic tools and treatment options for respiratory disease.

“Cannot get asleep all night”, “a little giddy” and other complaints in social networks can now be translated into formal medical terms, such as insomnia or vertigo, after a Russian-led study involving neural networks.

MIT computer scientists have developed a system that learns to identify objects within an image, based on a spoken description of the image. Given an image and an audio caption, the model will highlight in real-time the relevant regions of the image being described.

New machine-learning framework integrates personal genomes and health record data to predict aneurysm risk.

Artificial intelligence can help lung doctors interpret respiratory symptoms accurately and make a correct diagnosis.

Scientists have used machine learning to train computers to see parts of the cell the human eye cannot easily distinguish. Using 3D images of fluorescently labeled cells, the research team taught computers to find structures inside living cells without fluorescent labels, using only black and white images generated by an inexpensive technique known as brightfield microscopy.

A growing number of life science businesses are turning to greater supply chain collaboration for benefits like accelerated time to market, improved quality, reduced risk and more rapid and widespread innovation. But while 68% of executives in this industry say active and meaningful engagement with suppliers is essential to success, far too many, over a third, struggle to implement it.

New developments in pharmaceuticals have made new drugs and treatments available, enhancing options and quality of life for patients. Advanced data analytics solutions mean treatments are more effective and affordable, and less intrusive. However, these evolutions will mean major changes in how companies interact with patients and caregivers, and this will necessitate new capabilities for operations and supply chain.

Many recent advances in research have aimed to maximize the amount of data we can produce. But handling all that data is a challenge, and in analytical chemistry, data has more complexity and value than everyday spreadsheets, and tools matching that complexity will be needed to get data back into shape. We discussed how the field should approach these challenges with Andrew Anderson and , Graham McGibbon of Toronto-based analytical software supplier ACD/Labs.

The pharma industry is being disrupted in multiple ways. Data has never been more accessible and the speed at which it is flowing has left the industry reeling. Fundamental to the success of these advances is the need to integrate data from diverse sources and leverage predictive analytics to drive informed, real-time decisions. This article explores how pharma can maximize the value of different data types to advance research.

Scanning through medical records at the speed of light is currently just a dream. In the future we can hope for a Google-like tool for EHR.

From decluttering waiting rooms to assisting diagnostics, AI is changing the face of healthcare. In this article, Yaroslav Kuflinski looks over some of the software that is moving medicine forward.