The use of AI technology as a tool within the mental health field is relatively nascent. Though the uses vary, one AI role involves finding common factors or patterns in cases of people who may have attempted or died by suicide or expressed suicidal thoughts. AI then uses that data to create risk profiles that could help clinicians identify which patients may be at higher risk and may need more services, explained Adam Horwitz, an assistant professor at the University of Michigan Medical School who specializes in suicide prevention. AI tools are intended to complement, not replace, the work of clinicians who see patients, Horwitz said, and in fact, he noted, the U.S. Department of Veterans Affairs is already deploying the technology.

Researchers at the Allen Institute for Cell Science, a Seattle research group founded by Microsoft co-founder Paul Allen, have created the first predictive 3D model of a live human cell. Using the model, scientists can digitally visualize and even manipulate cell behavior on a computer screen. Called the Allen Integrated Cell, the model is the result of deep learning training with tens of thousands of high-quality cell images. It's able to identify subcellular structures and project a 3D, multilayered image of a cell that shows how all its components interact simultaneously -- something that has never been visualized in this way before. "To me, it's the closest thing I've ever seen in science to magic," said Rick Horwitz, Executive Director of the Allen Institute for Cell Science.

Three-dimensional views of human stem cells derived from skin showing DNA (blue), the cell membrane (purple) and other structures in yellow. No two stem cells are identical, even if they are genetic clones. This stunning diversity is revealed today in an enormous publicly available online catalogue of 3D stem cell images. The visuals were produced using deep learning analyses and cell lines altered with the gene-editing tool CRISPR. And soon the portal will allow researchers to predict variations in cell layouts that may foreshadow cancer and other diseases.

No two stem cells are identical, even if they are genetic clones. This stunning diversity is revealed today in an enormous publicly available online catalogue of 3D stem cell images. The visuals were produced using deep learning analyses and cell lines altered with the gene-editing tool CRISPR. And soon the portal will allow researchers to predict variations in cell layouts that may foreshadow cancer and other diseases. The Allen Cell Explorer, produced by the Allen Institute for Cell Science in Seattle, Washington, includes a growing library of more than 6,000 pictures of induced pluripotent stem cells (iPS) -- key components of which glow thanks to fluorescent markers that highlight specific genes.

Three-dimensional views of human stem cells derived from skin showing DNA (blue), the cell membrane (purple) and other structures in yellow. No two stem cells are identical, even if they are genetic clones. This stunning diversity is revealed today in an enormous publicly available online catalogue of 3D stem cell images. The visuals were produced using deep learning analyses and cell lines altered with the gene-editing tool CRISPR. And soon the portal will allow researchers to predict variations in cell layouts that may foreshadow cancer and other diseases.

A formal branch of artificial intelligence, machine-learning builds systems that learn directly from the data they are fed and effectively program themselves to analyse that data and make accurate predictions. Having already helped multiple business sectors create new models and drive competitive advantage, now it's the turn of the insurance industry. So just how is machine-learning changing the way insurers do business? "It gives insurers three distinct advantages," explains Max Richter, managing director in Accenture's UK insurance analytics group. "The first is to mine greater volumes of data, the second to scale analytics across the organisation by working smarter and faster, and lastly by answering more complex questions from'will this customer leave me at renewal?' to'what can I do about it?'" As such it is quickly becoming an essential tool for the insurance sector, specifically enabling companies to yield higher predictive accuracy as it can fit more flexible and complex models.

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