AI News, DeepMind, artificial intelligence and the future of the NHS

World Summit AI | Meet the world’s brightest AI brains | Oct 2019 | Amsterdam

As Global CTO for Sales, Patricia helps define mid and long term technology strategy, representing the needs of the broader Dell EMC ecosystem in strategic initiatives.

Patricia is the creator, author, narrator, and graphical influencer of the educational video series Dell EMC Big Ideas on emerging technologies and trends.

As CTO, Patricia was responsible for defining, and communicating the medium- to long-term vision EMC would embrace for delivering solutions to automate the management of Information Infrastructure resources.

In that capacity, Patricia was responsible for researching emerging technologies and for defining the strategy that Smarts would take in bringing solutions to market to address the challenges introduced by these technological advances.

Founded in 1955, the Columbia Engineering Board of Visitors is approved by the Columbia University Trustees to “advise and assist the Trustees, the Faculty of Engineering, and the Dean in the development of the school.” Patricia is the chairman of the advisory board for the Data Science Graduate Program at Worcester Polytechnic Institute where she will advise and assist in the school’s new program.

Artificial intelligence #03: AI in action – Moorfields case study

This is an edited version of an item first published on the Moorfields Eye Hospital NHS Foundation Trust website AI is already being used across the NHS in many ways to improve the early diagnosis of heart disease and lung cancer, to reduce the number of unnecessary operations performed due to false positives, to assist research by better-matching patients to clinical trials and supporting the planning of care for patients with complex needs.

Researchers from Moorfields and the UCL Institute of Ophthalmology have had a recent breakthrough in this research, published on Nature Medicine’s website, which describes how machine learning technology has been successfully trained on thousands of historic, de-personalised eye scans to identify signs of eye disease and recommend how patients should be referred for care.

“This shows the transformative research than can be carried out in the UK combining world-leading industry and NIHR/NHS hospital/university partnerships.” “Moorfields Eye Charity is proud to have funded equipment which underpins Dr Pearse Keane’s work as part of our programme of philanthropic support in pioneering researchers,”the charity’s chief executive, Robert Dufton, said.

“Artificial intelligence is showing the potential to transform the speed at which diseases can be diagnosed and treatments suggested, making the best use of the limited time of clinicians.” To find out more about Moorfields Eye Charity and the support provided, please visit their website here.

What are the pros and cons of AI?

“AI is a constellation of technologies - from machine learning to natural language processing - that allows machines to sense, comprehend, act and learn,” according to Accenture.

Many of us are familiar with the likes of Alexa, Cortana or Siri and we take for granted the fact that there’s quite a bit of sophisticated technology packed into compact devices to deliver the functionality these voice assistants offer.

These are systems which have already proven they can roundly beat even the most accomplished human competitors, with a poker-playing AI being the most recent example, beating five professional players at once.

One key use case of AI is mining data to help businesses, NGOs, governments and others make informed decisions on everything from strategy to product development, and to do so much more quickly than ever possible before.

AI is set to dominate the business, consumer and public sector landscape over the next few years with technologists predicting that soon we will be surrounded by IoT devices capable of performing mundane tasks and speeding up complex ones.

Online grocer Ocado uses automated machines in its warehouse, controlling thousands of robots, communicating with them 10 times a second to coordinate the movement of hundreds of thousands of crates.

An example of this is already in operation on many top range smartphones, where AI operates in the background constantly tweaking the phone's settings for maximum performance or battery life.

Job losses This is arguably the number one downside to AI consistently highlighted as a doom and gloom scenario were workers are laid off, unable to outperform machines.

“Many significant innovations in the past have been associated with a transition period of temporary job loss, followed by recovery, then business transformation and AI will likely follow this route,' said Svetlana Sicular, a Gartner research vice president.

'Unfortunately, most calamitous warnings of job losses confuse AI with automation - that overshadows the greatest AI benefit - AI augmentation - a combination of human and artificial intelligence, where both complement each other.'

Where sectors like healthcare and education were predicted to benefit, laborious positions such as manufacturing and transportation operators were estimated to see the largest decreases in jobs.

In what seems like the scary nightmares of a dystopian future, IBM's Watson has been using AI and Watson Analytics to decide if employees are worthy of a pay rise, a bonus or a promotion by looking at the experience and past projects of employees to judge the qualities and skills that individuals might have to serve the company in the future.

For example, Microsoft's ill-fated chatbot, Tay Tweets, had to be taken down after only 16 hours, after it started to tweet racist and inflammatory content – ideas it repeated from other Twitter users.

To surmount these hurdles, CIOs should set realistic expectations, identify suitable use cases and create new organisational structures.” Gartner advises that business and IT leaders should endeavour to cut the AI hype away from the reality by carefully considering and weighing up the opportunities vs risks.

“Amplifying our human intelligence with artificial intelligence has the potential of helping civilisation flourish like never before – as long as we manage to keep the technology beneficial.'

The Lancet Digital Health: First systematic review and meta-analysis suggests artificial intelligence may be as effective as health professionals at diagnosing disease

Artificial intelligence (AI) appears to detect diseases from medical imaging with similar levels of accuracy as health-care professionals, according to the first systematic review and meta-analysis, synthesising all the available evidence from the scientific literature published in The Lancet Digital Health journal.

Nevertheless, only a few studies were of sufficient quality to be included in the analysis, and the authors caution that the true diagnostic power of the AI technique known as deep learning--the use of algorithms, big data, and computing power to emulate human learning and intelligence--remains uncertain because of the lack of studies that directly compare the performance of humans and machines, or that validate AI's performance in real clinical environments.

What's more, only 25 studies validated the AI models externally (using medical images from a different population), and just 14 studies actually compared the performance of AI and health professionals using the same test sample,' explains Professor Alastair Denniston from University Hospitals Birmingham NHS Foundation Trust, UK, who led the research.

Despite strong public interest and market forces driving the rapid development of these technologies, concerns have been raised about whether study designs are biased in favour of machine learning, and the degree to which the findings are applicable to real-world clinical practice.

To provide more evidence, researchers conducted a systematic review and meta-analysis of all studies comparing the performance of deep learning models and health professionals in detecting diseases from medical imaging published between January 2012 and June 2019.

Analysis of data from 14 studies comparing the performance of deep learning with humans in the same sample found that at best, deep learning algorithms can correctly detect disease in 87% of cases, compared to 86% achieved by health-care professionals.

'So far, there are hardly any such trials where diagnostic decisions made by an AI algorithm are acted upon to see what then happens to outcomes which really matter to patients, like timely treatment, time to discharge from hospital, or even survival rates.'