After a baby is born, doctors sometimes examine the placenta--the organ that links the mother to the baby--for features that indicate health risks in any future pregnancies. Unfortunately, this is a time-consuming process that must be done by a specialist, so most placentas go unexamined after the birth. A team of researchers from Carnegie Mellon University (CMU) and the University of Pittsburgh Medical Center (UPMC) developed a machine learning approach to examine placenta slides so more women can be informed of their health risks. One reason placentas are examined is to look for a type of blood vessel lesions called decidual vasculopathy (DV). These indicate the mother is at risk for pre-eclampsia--a complication that can be fatal to the mother and the baby--in any future pregnancies.

Of all the ways COVID-19 has impacted healthcare -- such as the emergence of telehealth as a viable, useful solution for the provision of care -- artificial intelligence is having a bit of a moment. Per countless reports, AI is seeing rapid adoption throughout healthcare to identify solutions to protect against the pandemic and gain an advantage against the seemingly unmitigated spread of the virus. Sean Lane, the CEO of AI startup Olive, told Fierce Healthcare that AI will continue to accelerate at an unprecedented pace. "The pandemic is ... [accelerating] adoption of tech and the moment we've been waiting for in healthcare to embrace a future that is internet-like and changes the perception of what's necessary," Lane said. Work on the technology is taking place at every level -- from startups to tech goliaths and health systems and payers.

"Humans are good at recognizing anomalies, but they have their own biases or past experience," said senior author Rajiv Dhir, M.D., M.B.A., chief pathologist and vice chair of pathology at UPMC Shadyside and professor of biomedical informatics at Pitt. "Machines are detached from the whole story. To train the AI to recognize prostate cancer, Dhir and his colleagues provided images from more than a million parts of stained tissue slides taken from patient biopsies. Each image was labeled by expert pathologists to teach the AI how to discriminate between healthy and abnormal tissue. The algorithm was then tested on a separate set of 1,600 slides taken from 100 consecutive patients seen at UPMC for suspected prostate cancer. During testing, the AI demonstrated 98% sensitivity and 97% specificity at detecting prostate cancer -- significantly higher than previously reported for algorithms working from tissue slides. Also, this is the first algorithm to extend beyond cancer detection, reporting high performance for tumor grading, sizing and invasion of the surrounding nerves. These all are clinically important features required as part of the pathology report. AI also flagged six slides that were not noted by the expert pathologists. But Dhir explained that this doesn't necessarily mean that the machine is superior to humans. For example, in the course of evaluating these cases, the pathologist could have simply seen enough evidence of malignancy elsewhere in that patient's samples to recommend treatment. For less experienced pathologists, though, the algorithm could act as a failsafe to catch cases that might otherwise be missed. "Algorithms like this are especially useful in lesions that are atypical," Dhir said. "A nonspecialized person may not be able to make the correct assessment.

The first hospital network in the U.S. has joined an international clinical trial using artificial intelligence to help determine which treatments for patients with the novel coronavirus are most effective on an on-going basis. Why it matters: In the midst of a pandemic, scientists face dueling needs: to find treatments quickly and to ensure they are safe and effective. By using this new type of adaptive platform, doctors hope to collect clinical data that will help more quickly determine what actually works. State of play: No treatments have been approved for COVID-19 yet. Researchers have made headway in mapping how the virus attaches and infects human cells -- helping "guide drug developers, atom by atom, in devising safe and effective ways to treat COVID-19," National Institutes of Health director Francis Collins writes.

The journal continues to take on big and tough issues as exemplified by the November 2018 special issue "Machine Learning in Health and Biomedicine." As computational power increases exponentially, the capacity to (more affordably) handle, store, and analyze "big data" using machine learning (ML) will revolutionize science and medicine. The power of ML is to find patterns among variables in large data sets rather than being programmed with rules. Models become more complex when they move from supervised (input and outputs have labels) to unsupervised (no labels), and when they move from linear regression with decision trees to neural networks ( 3 neural networks is termed deep learning). As the complexity increases so does one's ability to "interpret" the data.

Eventually, many conversations about artificial intelligence (AI) include HAL. An acronym for Heuristically programmed ALgorithmic computer, HAL played a prominent and disconcerting role in Stanley Kubrick's mind-bending 1968 film 2001: A Space Odyssey. In the film, sentient computer HAL learns that the humans suspect it of being in error and will disconnect it should that error be confirmed. Of course, HAL is having none of that, and terror ensues. So influential was Kubrick's adaptation of an Arthur C. Clarke short story that HAL is now a part of the ways in which AI is often conceived.

Investment in artificial intelligence (A.I.) has skyrocketed over the past several years. One study suggests 80 percent of the enterprises it surveyed have some form of A.I. in production today and 30 percent plan to expand A.I. investment over the next 36 months. Health care has the most robust A.I. startup scene of any sector: as of February 2017, there were 106 A.I. startups in the industry. Seventy launched in the last year alone. While there is tremendous excitement surrounding A.I. activity, there is also considerable fear, confusion and resistance.

With so many commercialized algorithms on the market, many health systems have an important decision to make: Should they buy an artificial intelligence (AI) model or build their own? If a health system elects to build its own model, it has to invest time and manpower into it. But the benefits could be tremendous. The case for building a customized AI model is simple: Instead of the algorithm learning on national data, it is learning on the health system's data, said Pamela Peele, Ph.D., chief analytics officer at UPMC insurance division and UPMC enterprises. She spoke during a World Health Care Congress keynote called "More than Buzz: Realize the Potential of AI and Machine Learning."

PITTSBURGH, March 12, 2019 - Lung cancer is the leading cause of cancer deaths worldwide. Screening is key for early detection and increased survival, but the current method has a 96 percent false positive rate. Using machine learning, researchers at the University of Pittsburgh and UPMC Hillman Cancer Center have found a way to substantially reduce false positives without missing a single case of cancer. The study was published today in the journal Thorax. This is the first time artificial intelligence has been applied to the question of sorting out benign from cancerous nodules in lung cancer screening.

Vinod Khosla, a legendary Silicon Valley investor, argues that robots will replace doctors by 2035. And there is some evidence that he may be right. A 2017 study out of the Massachusetts General Hospital and MIT showed that an artificial intelligence (AI) system was equal or better than radiologists at reading mammograms for high risk cancer lesions needing surgery. A year earlier, and reported by the Journal of the American Medical Association, Google showed that computers are similar to ophthalmologists at examining retinal images of diabetics. And recently, computer-controlled robots performed intestinal surgery successfully on a pig.