If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
More organizations are adopting artificial intelligence (AI). Fourteen percent of global CIOs have already deployed AI and 48% will deploy it in 2019 or by 2020, according to Gartner's 2019 CIO Agenda survey. "While adoption is increasing, some organizations are still questioning the business impact and benefits. Today, we witness three barriers to the adoption of AI," says Brian Manusama, Senior Director Analyst, Gartner. The first barrier is skills.
The Major League Baseball team, the Oakland Athletics, for many years a poorly performing and unsuccessful team, surprised everybody during the 2000-2001 season. The team had a remarkable run of wins, led the Major League in performance, and broke a number of League records. Michael Lewis, in his bestselling book, "Moneyball: The Art of Winning an Unfair Game", describes the secret to this turnaround. The A's new, young and unexperienced manager, Billy Beane, "revolutionized" the team's roster by releasing a number of star players (despite the protests of his closest advisors) and instead, signing a number of unknown players. He chose these players by using a model/algorithm that was based on several professional, well-defined baseball parameters.
AI in medical imaging entered the consciousness of radiologists just a few years ago, notably peaking in 2016 when Geoffrey Hinton declared radiologists' time was up, swiftly followed by the first AI startups booking exhibiting booths at RSNA. Three years on, the sheer number and scale of AI-focussed offerings has gathered significant pace, so much so that this year a decision was made by the RSNA organising committee to move the ever-growing AI showcase to a new space located in the lower level of the North Hall. In some ways it made sense to offer a larger, dedicated show hall to this expanding field, and in others, not so much. With so many startups, wiggle room for booths was always going to be an issue, however integration of AI into the workflow was supposed to be a key theme this year, made distinctly futile by this purposeful and needless segregation. By moving the location, the show hall for AI startups was made more difficult to find, with many vendors verbalising how their natural booth footfall was not as substantial as last year when AI was upstairs next to the big-boy OEM players. One witty critic quipped that the only way to find it was to'follow the smell of burning VC money, down to the basement'.
In a recent speech, Forrester vice president and principal consultant Huard Smith said that the human aspect of many professions would be "all gone" by 2030 due to advances in AI and ML technology. In this piece, I'll look at seven of the industries or positions that are currently most likely to decline over the next decade. Believe me; number seven will surprise you. The chances of this particular role becoming fully computerized are as high as 99.9%. This is mainly because telemarketing conversion rates are relatively low.
When the X-ray was discovered at the end of the 19th century, a new medical discipline was born. Radiology became a way to study, diagnose and treat disease. Today, expertise among radiologists, radiation oncologists, nuclear medicine physicians, medical physicists and technicians includes many forms of medical imaging--from diagnostic and cancer imaging to mammography, radiation therapy, ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI). As we move into the third decade of the 21st century, radiology--perhaps more than any other medical specialty--is poised for transformation. Thanks to artificial intelligence (AI), radiologists foresee a future in which machines enhance patient outcomes and avoid misdiagnosis.
No one knows who gave Rahul Roy tuberculosis. Roy's charmed life as a successful trader involved traveling in his Mercedes C class between his apartment on the plush Nepean Sea Road in South Mumbai and offices in Bombay Stock Exchange. He cared little for Mumbai's weather. He seldom rolled down his car windows – his ambient atmosphere, optimized for his comfort, rarely changed. Historically TB, or "consumption" as it was known, was a Bohemian malady; the chronic suffering produced a rhapsody which produced fine art. TB was fashionable in Victorian Britain, in part, because consumption, like aristocracy, was thought to be hereditary. Even after Robert Koch discovered that the cause of TB was a rod-shaped bacterium – Mycobacterium Tuberculosis (MTB), TB had a special status denied to its immoral peer, Syphilis, and unaesthetic cousin, leprosy. TB became egalitarian in the early twentieth century but retained an aristocratic noblesse oblige. George Orwell may have contracted TB when he voluntarily lived with miners in crowded squalor to understand poverty. Unlike Orwell, Roy had no pretentions of solidarity with poor people. For Roy, there was nothing heroic about getting TB. He was embarrassed not because of TB's infectivity; TB sanitariums are a thing of the past. TB signaled social class decline. He believed rickshawallahs, not traders, got TB.
Analyzing chest X-ray images with machine learning algorithms is easier said than done. That's because typically, the clinical labels required to train those algorithms are obtained with rule-based natural language processing or human annotation, both of which tend to introduce inconsistencies and errors. Additionally, it's challenging to assemble data sets that represent an adequately diverse spectrum of cases, and to establish clinically meaningful and consistent labels given only images. In an effort to move forward the goalpost with respect to X-ray image classification, researchers at Google devised AI models to spot four findings on human chest X-rays: pneumothorax (collapsed lungs), nodules and masses, fractures, and airspace opacities (filling of the pulmonary tree with material). In a paper published in the journal Nature, the team claims the model family, which was evaluated using thousands of images across data sets with high-quality labels, demonstrated "radiologist-level" performance in an independent review conducted by human experts.
The Radiological Society of North America (RSNA) is an international society of radiologists, medical physicists, and other medical professionals. The annual RSNA meeting with approximately 50,000 attendees in Chicago is a place for scientific exchange and clinical training. This year radiologists are invited to experience the hands-on cutting-edge technology of artificial intelligence, 3D printing, and virtual reality. The Fraunhofer MEVIS team will be pleased to welcome you at their booth, located at the "AI Showcase". Our experts are looking forward to providing you with a range of latest developments in deep learning and artificial intelligence, e.g., our free software MEVIS draw.
Nuance has released an upgraded version of its PowerScribe One cloud platform for radiologists. The solution leverages Nuance's conversational AI tech to facilitate a range of clinical and administrative functions. The latest update introduces an Ambient Mode that sorts free-form dictation into more organized reports, as well as a dark mode that is designed to reduce fatigue. The latter was designed over the course of 18 months with input from radiologists and user interface experts with an eye towards the long hours that radiologists spend in reading rooms. Other highlights include better data synchronization with third-party platforms, and an improved virtual assistant that allows radiologists to request electronic health records (EHR), send messages, and carry out other tasks with basic vocal commands.
It's safe to say there are too many manual processes in medicine. While in training, I hand wrote lab values, diagnoses, and other chart notes on paper. I always knew this was an area in which technology could help improve my workflow and hoped it would also improve patient care. Since then, advancements in electronical medical records have been remarkable, but the information they provide is not much better than the old paper charts they replaced. If technology is to improve care in the future, then the electronic information provided to doctors needs to be enhanced by the power of analytics and machine learning.