Pacific Grove, California--Pop a few human stem cells into culture, provide the right molecular signals, and before long a mock cerebral cortex or a cerebellum knockoff could be floating in the medium. These neural, or brain, organoids, typically just a few millimeters across, are not "brains in a dish," as some journalists have described them. But they are becoming ever more sophisticated and true to life, capturing more of the brain's cellular and structural intricacy. "It's surprising how far this [area] has advanced in the last year," says John Evans, a sociologist at the University of California San Diego who follows the research and public opinions on it. That progress has allowed researchers to delve deeper into how the human brain develops, functions, and goes awry in diseases, but it has also sharpened ethical questions.

In this special guest feature, Dr George Magrath, CEO of Lexitas, explains how AI is being harnessed in the development of eye care medicines. Lexitas is a 160-person company which partners with pharmaceutical firms to develop novel eye care drugs by running clinical trials. Dr Magrath is a trained ophthalmologist who, despite his busy day-to-day work leading Lexitas, still takes one day out every week to maintain his practice and treat patients with rare eye conditions. In fact, he is the only physician in his home state of South Carolina with fellowship training in treating cancers in and around the eye. The potential of AI to make people healthier is revolutionary.

Late one January afternoon, British pharmacologist Peter Richardson ran out of his home office and told his wife, "Got it!" She asked what he was talking about and offered a cup of tea. Richardson explained that he had identified a drug that might help people infected with a new virus spreading in China. Richardson's dash was prompted by a finding from artificial intelligence software developed by his employer, BenevolentAI, a London startup where he is vice president of pharmacology. The company has created a kind of search engine on steroids that combines drug industry data with nuggets gleaned from scientific research papers.

In a rush to find solutions for the Covid-19 pandemic, researchers are deploying machine learning algorithms to trawl through data that might give us more clues about the virus. Some claim to have identified potential treatments based on the data, while others are using it to screen patients or identify those at highest risk. But, like their vaccine and drug counterparts, many of these algorithms are still unproven. With hundreds of research articles describing the use of artificial intelligence or machine learning -- many of them preprints -- it can be difficult to sort out which ones are most effective. "I've heard a lot of hype about machine learning being applied to battling Covid-19, but I haven't seen very many concrete examples where you could imagine in the short- or medium-term something that is going to have a substantial effect," said John Quackenbush, chair of the Department of Biostatistics at the Harvard T.H. Chan School of Public Health, in a phone interview.

To battle the novel coronavirus that's been linked to Wuhan, China, researchers are using artificial intelligence to discover potential treatments, including already-approved drugs and completely new compounds. At the same time, the pneumonia-like illness has only gotten worse. As of Friday morning, the 2019-nCoV coronavirus had taken the lives of more than 600 people and infected more than 30,000, with cases documented in at least 25 countries. Researchers at the British artificial intelligence startup Benevolent AI say they used the tech to search for existing approved drugs that might be helpful in limiting the virus's infection. Another set of scientists affiliated with Deargen, a drug discovery company based in South Korea, say that they used deep learning to find various available antiviral drugs that could be investigated as a potential treatment (that research has not yet been peer-reviewed). Meanwhile, a Maryland-based biotech company, Insilico, said it used AI to come up with new molecules that could serve as potential medications, and it will now synthesize and test 100 of the compounds, according to Fortune.

Human biology is full of surprises -- especially for drug makers. Viagra wasn't designed for erectile dysfunction. Both drugs were meant to treat cardiovascular issues (as sildenafil and minoxidil, respectively), until patients reported their sexual and follicular side effects. When his son was diagnosed with an ultra-rare disease, computer scientist Matt Might, Ph.D., kicked off a search for answers. His quest led to partnerships with researchers across the country, a White House appointment, a faculty position at Harvard, and a profile in the New Yorker.

Web users searching for photos and cops looking for suspects in video already benefit from software that understands the content of images. Chris Gibson says it can also make it easier to find treatments for diseases not targeted by existing drugs. "By combining robotics and machine vision, we can work at large scale on hundreds of diseases simultaneously, using a small number of people," says Gibson, who is CEO and cofounder of the 40-person startup Recursion Pharmaceuticals. Recursion uses software to read out the results of high-throughput screening, which automates drug testing in cells. That isn't a new idea, but Recursion uses algorithms that inspect cells at an unusual level of detail.

Web users searching for photos and cops looking for suspects in video already benefit from software that understands the content of images. Chris Gibson says it can also make it easier to find treatments for diseases not targeted by existing drugs. "By combining robotics and machine vision, we can work at large scale on hundreds of diseases simultaneously, using a small number of people," says Gibson, who is CEO and cofounder of the 40-person startup Recursion Pharmaceuticals. Recursion uses software to read out the results of high-throughput screening, which automates drug testing in cells. That isn't a new idea, but Recursion uses algorithms that inspect cells at an unusual level of detail.