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) …
Dr. Albert Hsiao and his colleagues at the University of California–San Diego health system had been working for 18 months on an artificial intelligence program designed to help doctors identify pneumonia on a chest X-ray. When the coronavirus hit the United States, they decided to see what it could do. The researchers quickly deployed the application, which dots X-ray images with spots of color where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and it's providing some value in diagnosis, said Hsiao, the director of UCSD's augmented imaging and artificial intelligence data analytics laboratory. His team is one of several around the country that has pushed AI programs developed in a calmer time into the COVID-19 crisis to perform tasks like deciding which patients face the greatest risk of complications and which can be safely channeled into lower-intensity care.
The artificial intelligence model showed great promise in predicting which patients treated in U.S. Veterans Affairs hospitals would experience a sudden decline in kidney function. But it also came with a crucial caveat: Women represented only about 6% of the patients whose data were used to train the algorithm, and it performed worse when tested on women. The shortcomings of that high-profile algorithm, built by the Google sister company DeepMind, highlight a problem that machine learning researchers working in medicine are increasingly worried about. And it's an issue that may be more pervasive -- and more insidious -- than experts previously realized, new research suggests. The study, led by researchers in Argentina and published Monday in the journal PNAS, found that when female patients were excluded from or significantly underrepresented in the training data used to develop a machine learning model, the algorithm performed worse in diagnosing them when tested across across a wide range of medical conditions affecting the chest area.
Drawing on the records of nearly 600,000 Chinese patients who had visited a pediatric hospital over an 18-month period, the vast collection of data used to train this new system highlights an advantage for China in the worldwide race toward artificial intelligence. Because its population is so large -- and because its privacy norms put fewer restrictions on the sharing of digital data -- it may be easier for Chinese companies and researchers to build and train the "deep learning" systems that are rapidly changing the trajectory of health care. On Monday, President Trump signed an executive order meant to spur the development of A.I. across government, academia and industry in the United States. As part of this "American A.I. Initiative," the administration will encourage federal agencies and universities to share data that can drive the development of automated systems. Pooling health care data is a particularly difficult endeavor.
From predicting outbreaks to devising treatments, doctors are turning to AI in an effort to combat the COVID-19 pandemic. Why it matters: While machine learning algorithms were already becoming a part of health care, COVID-19 is likely to accelerate their adoption. But lack of data and testing time could hinder their effectiveness -- for this pandemic, at least. What's happening: With millions of cases and outbreaks in every corner of the world, speed is of the essence when it comes to diagnosing and treating COVID-19. So it's no surprise doctors were quick to employ AI tools in an effort to get ahead of what could be the worst pandemic in a century.
The coronavirus pandemic will cause us to rethink major aspects of everyday life around the world, but it may also expedite the use of artificial intelligence in health care. Sinovation Ventures CEO Kai-Fu Lee explains how the revolution has already begun, and how things like diagnosis, drug discovery and even robot delivery will progress due to current global health conditions. Not everyone has the mental capacity to self-isolate for weeks during a pandemic. Some people are just lonely. Others choose to spend time with friends and family while adhering to social distancing guidelines.
Despite what you may have heard, from the World Health Organization or World Economic Forum or wherever else, India has not done a good job of containing the coronavirus. According to the country's Ministry of Health and Welfare, India now has over 110,000 cases, having already surpassed the total of the only country with more people, China. The total death toll, as of this writing, stands at about 3,500. In a country of 1.3 billion people, these may seem like small numbers. But they do not actually give a full accounting of the virus's toll.
Scientists believe Artificial Intelligence can free humanity from performing routine tasks in many areas. Healthcare is that area that seems to need these changes the most. While outside it is already 2020, and the majority of businesses are digitalizing themselves. The healthcare industry remains a pain point for the biggest part of the world. The research says that 56% of hospitals don't have a strategy on how to govern data and conduct analytics.
Our second meeting of 2020 will focus on how Artificial Intelligence (AI) can be used in the field of genomics to help to develop personalised medicines and treatments to improve patient outcomes. AI has come a long way in health care and it has many different uses, one of those applied to genomics, by identifying individuals' phenotypes and genotypes health care professionals can offer personalised medicine, tailoring the right therapeutic strategy for the right person at the right time. AI can also determine an individual's predisposition to a disease, offering him/her timely prevention. The concept behind personalised medicine is to customize therapies to ensure they are tailored for patients based on their own unique genomic profile. But the cost of processing and storing every citizen's fully sequenced genome might be significantly costly.
SARS-COV-2 has upended modern health care, leaving health systems struggling to cope. Addressing a fast-moving and uncontrolled disease requires an equally efficient method of discovery, development and administration. Artificial Intelligence (AI) and Machine Learning driven health care solutions provide such an answer. AI-enabled health care is not "the medicine of the future," nor does it mean robot doctors rolling room to room in hospitals treating patients. Instead of a hospital from some future Jetsons-like fantasy, AI is poised to make impactful and urgent contributions to the current health care ecosystem.
Machine vision, natural language processing, data analytics and other deep learning applications will propel global AI software revenues over the next five years via a growing list of industry segments spanning automotive and health care to financial services and retail. Market tracker Omdia forecasts AI software revenues will surge through 2025 to $126 billion, a 12-fold increase over a $10.1 billion industry in 2018. "The narrative is shifting from asking whether AI is viable to declaring that AI is now a requirement for most enterprises that are trying to compete on a global level," said Keith Kirkpatrick, principal analyst with Omdia. "AI is likely to trigger major transformations in industries where there is a clear case for incorporating AI, rather than in pie-in-the-sky use cases that may not generate a return on investment for many years," Kirkpatrick added. Omdia estimates that more than half of AI revenues will be generated by machine vision and language applications, with deep learning deployments driving the AI market.