BACKGROUND Deep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of automated deep learning software to develop medical image diagnostic classifiers by health-care professionals with no coding--and no deep learning--expertise. METHODS We used five publicly available open-source datasets: retinal fundus images (MESSIDOR); optical coherence tomography (OCT) images (Guangzhou Medical University and Shiley Eye Institute, version 3); images of skin lesions (Human Against Machine [HAM] 10000), and both paediatric and adult chest x-ray (CXR) images (Guangzhou Medical University and Shiley Eye Institute, version 3 and the National Institute of Health [NIH] dataset, respectively) to separately feed into a neural architecture search framework, hosted through Google Cloud AutoML, that automatically developed a deep learning architecture to classify common diseases. Sensitivity (recall), specificity, and positive predictive value (precision) were used to evaluate the diagnostic properties of the models. The discriminative performance was assessed using the area under the precision recall curve (AUPRC).

Biotechnology, pharmaceutical and life sciences industries are where applied artificial intelligence (AI) can greatly accelerate innovation and shorten the product development life-cycle. Developing a drug typically takes 10 to 15 years on average, with only approximately 12 percent of drugs in clinical trials ultimately gaining U.S. Food and Drug Administration (FDA) approval. In an AI milestone in life sciences, Insilico Medicine announced a new machine learning tool for drug discovery that can generate a novel molecule in days instead of years, and published their findings in Nature Biotechnology on September 2, 2019. Insilico Medicine is a venture-backed start-up with multiple investors that include WuXi AppTec, Juvenescence, Peter Diamandis' BOLD Capital Partners, and Pavilion Capital. Led by CEO and Founder Alex Zhavoronkov, the company's mission is to extend longevity by applied AI solutions for drug discovery and aging research.

Four big names in radiology--Julie Sogani, MD, of Weill Cornell Medicine; Bibb Allen Jr., MD, of Grandview Medical Center in Birmingham, Alabama; Keith Dreyer, DO, PhD, with the ACR's Commission on Informatics; and Geraldine McGinty, MD, MBA, part of the ACR's Board of Chancellors--combined to define AI in radiology, entertain the idea of an AI imaging'ecosystem' and describe how the field will need to overcome challenges to use the technology in clinical practice.

From job killer to killer robot, artificial intelligence (AI) increasingly has come under the spotlight for its potentially adverse impact on human lives. Singapore, however, is advocating the need to hold off judgement whilst the technology continues to evolve and focus instead on building trust. Whilst not a new concept, AI in recent years had been garnering significant interest due to the convergence of three key factors, said S. Iswaran, Singapore's Minister for Communications and Information and Minister-in-charge of Trade Relations. First was the ability now to amass large volumes of data, organise, and use it. Computing power in large quantities also had become more available and at lower costs.

IMAGE: This is Brittany Dugger choosing a sample from the UC Davis Alzheimer's Disease Center's brain bank. A team of researchers lead by Brittany Dugger of UC Davis Health has been awarded a $3.8 million grant from the National Institute for Aging (NIA) to help define the neuropathology of Alzheimer's disease in Hispanic cohorts. The five-year grant will fund the first large-scale initiative to present a detailed description of brain manifestations of the Alzheimer's disease in individuals of Mexican, Cuban, Puerto Rican, and Dominican descent. Hispanics, one of the fastest growing demographic groups in the U.S., have a higher risk of dementia than Non-Hispanic Whites (NHWs). The demographic, genetic and environmental differences between individuals of Hispanic descent and NHWs can lead to different levels of disease risk and presentation.

In an bid to reduce the effects of food waste further, millions of trees are being planted across the globe, with Ethiopia recently setting a record of 30 million trees in a day. But while global efforts to plant new trees are necessary, British start-up Biocarbon Engineering have identified issues. Planting by hand isn't the most effective; it's extremely time consuming, and therefore the number of trees that can be planted is limited. This is where artificial intelligence comes in.

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Apple has programmed its Siri voice assistant to avoid politically charged subjects, and deflect or duck questions that require its AI to take a stand on issues, it emerged this week. From a tranche of documents leaked by a former contract worker who evaluated Siri responses to user questions for accuracy, The Guardian obtained a set of guidelines drawn up last year to ensure Siri's responses to "sensitive" topics comes across as neutral. In keeping with these guidelines, Siri's responses were revised to endorse "equality" while avoiding the word "feminism," even if asked directly. Where once Siri responded to the question, "Are you a feminist?" The leaked guidelines reportedly state, "Siri should be guarded when dealing with potentially controversial content."

With an immense potential to bring a transformation in quality, access, and cost, healthcare artificial intelligence market is witnessing a significant global popularity. The implementation of artificial intelligence in the medical fraternity is advancing at a rapid pace. The remarkable expansion of the market in a relatively shorter span is perhaps of no surprise today as artificial intelligence (AI) has enabled smoothening of almost every operation that a healthcare organization needs. Operational and financial turbulence due to the increasing labor costs, growing patient base, digitization, and rising demand for interoperability are some of the challenges that have primarily driven healthcare artificial intelligence market so far. As per reliable statistics, almost 35% of the overall healthcare organizations are planning to leverage AI within next two years.

The ability of governments and law enforcement agencies to monitor the public using facial recognition was once the province of dystopian science fiction. But modern technology is increasingly bringing versions of these scenarios to life. A recent investigation found that U.S. law enforcement agencies are using state Department of Motor Vehicles records to identify individual Americans without their consent, including those with no criminal record. And countries such as China have made facial recognition technology a cornerstone of their strategies to police the behaviors and activities of their publics. Despite these high-profile examples from fiction and reality, a new Pew Research Center survey finds that a majority of Americans (56%) trust law enforcement agencies to use these technologies responsibly.