It still remains to be seen whether the sci-fi genre is correct and artificial intelligence will one day rise up against the human race, but in the meantime, AI just might save your life. An algorithm developed by the Mayo Clinic can significantly increase the number of cases of low ejection fraction caught in its earliest stages, when it's still most treatable, according to a study published this month in Nature Medicine. The condition, in which the heart is unable to pump enough blood from its chamber with each contraction, is associated with cardiomyopathy and heart failure and is often symptomless in its early stages. Traditionally, the only way to diagnose low ejection fraction is with the use of an echocardiogram, a time-consuming and expensive cardiac ultrasound. The Mayo Clinic's AI algorithm, however, can screen for low ejection fraction in a standard 12-lead electrocardiogram (EKG) reading, which is a much faster and more readily available tool. In the study, more than 22,600 patients received an EKG as part of their usual primary care checkups, then were randomly assigned to have their results analyzed by the AI or by a physician as usual.

Life Whisperer, the fertility arm of AI healthcare company Presagen, has made a significant breakthrough in using artificial intelligence to non-invasively help embryologists rank and select genetically healthy embryos in IVF. Currently, PGT-A genetic testing requires a portion of a healthy embryo to be removed and sent away for testing. The procedure is invasive, costly, and potentially risky. Presagen CEO, Dr Michelle Perugini said: "Some future parents are just not comfortable with the thought of having to biopsy their embryo, which may ultimately become their baby. PGT-A testing is conducted because evidence suggests genetically healthy embryos can increase pregnancy success and reduce miscarriage for IVF patients who are desperate for children."

A novel device designed to help stroke patients recover wrist and hand function has been approved by the US Food and Drug Administration (FDA). Called IpsiHand, the system is the first brain-computer interface (BCI) device to ever receive FDA market approval. The IpsiHand device consists of two separate parts – a wireless exoskeleton that is positioned over the wrist, and a small headpiece that records brain activity using non-invasive electroencephalography (EEG) electrodes. The system is based on a discovery made by Eric Leuthardt and colleagues at the Washington University School of Medicine over a decade ago. It is well known that each side of the brain controls movement on the opposite side of the body, so if a stroke damages motor function on the right side of the brain movement on a person's left side will be affected.

To give clinicians a quick, cross-sectional look into potential blockages of the heart's major arteries, Abbott has combined digital imaging technology with artificial intelligence to build an automated system for cardiac procedures. The company's Ultreon software relies on catheters equipped with optical coherence tomography, which uses laser light to scan the interior of a blood vessel and the immediately surrounding tissues to detect calcium and plaque deposits, while also instantly measuring the diameter of an artery. The system--which has now received a CE Mark in Europe--is designed to provide surgeons with prompt information during the placement of coronary stents, faster and more precisely compared to conventional angiography imaging. A previous study by Abbott found having the information from OCT scans readily available led most physicians to change their treatment approach, by selecting the proper stent size and placement location. RELATED: FDA clears PhotoniCare's handheld OCT scanner for checking ear infections After planning a procedure using angiography alone, 88% of operations altered course when surgeons saw high-resolution OCT images and automatic measurements from inside the patient's arteries.

The developed world is on the cusp of turning the corner in the fight against COVID-19 thanks to the unprecedented effort to rapidly develop and distribute effective vaccines. Now technologists are hoping to take drug development to the next level, and AI will play a big role. One of the companies at the forefront of using machine learning and AI to develop drugs is CytoReason. The company helps pharmaceutical firms like Pfizer accelerate drug development by providing high resolution models of the human body that's infected with the disease that the drug companies are targeting. "If I told you that in 200 years, drugs would be developed in a computer, you would not be real surprised," said CytoReason CEO and founder David Harel.

Artificial intelligence companies are developing audio transcription tools that can create searchable archives of calls and meetings, WIRED reported April 15. Artificial intelligence companies have greatly improved their automated audio transcription in recent years, and the technology is now able to produce transcripts with impressive accuracy, according to WIRED. One example is Stedi, a company that makes business-to-business software. It developed a tool called Rewatch that records meetings and uses voice-dictation AI to transcribe it, providing employees with a searchable record of everything said during the meeting. AI companies Otter.ai and Trint also offer voice-dictation to produce meeting transcripts, and Zoom has built-in wares that offer meeting notes.

In just the last two years, artificial intelligence has become embedded in scores of medical devices that offer advice to ER doctors, cardiologists, oncologists, and countless other health care providers. The Food and Drug Administration has approved at least 130 AI-powered medical devices, half of them in the last year alone, and the numbers are certain to surge far higher in the next few years. Several AI devices aim at spotting and alerting doctors to suspected blood clots in the lungs. Some analyze mammograms and ultrasound images for signs of breast cancer, while others examine brain scans for signs of hemorrhage. Cardiac AI devices can now flag a wide range of hidden heart problems.

Today, the U.S. Food and Drug Administration authorized marketing of the GI Genius, the first device that uses artificial intelligence (AI) based on machine learning to assist clinicians in detecting lesions (such as polyps or suspected tumors) in the colon in real time during a colonoscopy. "Artificial intelligence has the potential to transform health care to better assist health care providers and improve patient care. When AI is combined with traditional screenings or surveillance methods, it could help find problems early on, when they may be easier to treat," said Courtney H. Lias, Ph.D. acting director of the GastroRenal, ObGyn, General Hospital and Urology Devices Office in the FDA's Center for Devices and Radiological Health. "Studies show that during colorectal cancer screenings, missed lesions can be a problem even for well-trained clinicians. With the FDA's authorization of this device today, clinicians now have a tool that could help improve their ability to detect gastrointestinal lesions they may have missed otherwise."

Michael Wallace has performed hundreds of colonoscopies in his 20 years as a gastroenterologist. He thinks he's pretty good at recognizing the growths, or polyps, that can spring up along the ridges of the colon and potentially turn into cancer. Sometimes the polyps are flat and hard to see. Other times, doctors just miss them. "We're all humans," says Wallace, who works at the Mayo Clinic.

Data-driven artificial intelligence models fed with published scientific findings have been used to create powerful prediction engines for scientific and technological advance, such as the discovery of novel materials with desired properties and the targeted invention of new therapies and vaccines. These AI approaches typically ignore the distribution of human prediction engines -- scientists and inventor -- who continuously alter the landscape of discovery and invention. As a result, AI hypotheses are designed to substitute for human experts, failing to complement them for punctuated collective advance. Here we show that incorporating the distribution of human expertise into self-supervised models by training on inferences cognitively available to experts dramatically improves AI prediction of future human discoveries and inventions. Including expert-awareness into models that propose (a) valuable energy-relevant materials increases the precision of materials predictions by ~100%, (b) repurposing thousands of drugs to treat new diseases increases precision by 43%, and (c) COVID-19 vaccine candidates examined in clinical trials by 260%. These models succeed by predicting human predictions and the scientists who will make them. By tuning AI to avoid the crowd, however, it generates scientifically promising "alien" hypotheses unlikely to be imagined or pursued without intervention, not only accelerating but punctuating scientific advance. By identifying and correcting for collective human bias, these models also suggest opportunities to improve human prediction by reformulating science education for discovery.