KPMG survey has revealed that 53% of healthcare executives believe the healthcare industry is ahead of most other sectors in the adoption of artificial intelligence (AI)

The latest report from KPMG has stated that more than half (53%) of executives say that when it comes to the adoption of AI, the healthcare sector is quite ahead to other industries. As per the report, 37% of healthcare executives believe factors like cost and skill barriers are slowing the AI implementation in the healthcare industry.

The Critical Role of Cyber Security in Healthcare

The adoption of AI and automation in hospital systems has increased exponentially since 2017, found the report. Nearly 90% of respondents believe AI is already creating efficiencies in their operations, and 91% say AI is expanding patient access to care. AI will be effective in diagnosing patient illnesses, say 68% of respondents. Meanwhile, 47% believe diagnostics will have a significant impact by 2022, according to the study. Respondents also said AI would have a positive effect on process automation. Around 40% of them think AI will assist providers with better X-rays and CT scans. It will continue to advance the digitization of healthcare as per the KPMG survey. With the help of AI technologies, 41% of respondents expect improved records management, while 48% believe the most significant impact of AI will be in biometric-related applications.

In the field of healthcare and diagnostics, studies have shown that AI can assist doctors while making informed decisions, and enhance patient diagnostics, even to the extent of identification of cancer. Nearly half of healthcare executives said their institutions offer AI training courses to employees, and 67% say their employees support AI adoption.

On the flip side, there has also been suspicion that AI has increased the overall cost of healthcare more than half of the surveys respondents feel this way. This suggests that healthcare executives are still trying to determine the most cost-effective areas to use AI tools. Two of the major concerns for healthcare companies are privacy and security. According to the survey, 75% of respondents have concerns that AI could threaten the privacy and security of patient data, while 86% say their companies are taking steps to protect patient privacy as they implement AI.

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Healthcare leaders agree that AI will play a key role in improving care delivery, with 90% of respondents saying they believe that AI will improve the patient experience. The results show that once leaders address key issues to implementation, the benefits of AI could outweigh potential risks. Applying AI to unstructured data will also be quite useful in the diagnosis and more accurate prognosis of health issues. Supported by doctors, AI could well be the closest tool to increase the accuracy of healthcare analysis and provide much more error-free results in days to come.

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Artificial Intelligence Contributes Higher in Healthcare Compared to Other Industries - EnterpriseTalk

The use of artificial intelligence is now the norm in many industries, from integrating the technology in autonomous vehicles for safety, to AI algorithms being used to improve advertising campaigns. But, by using it in healthcare, could it also help us predict the outbreak of a virus such as the COVID-19 coronavirus?

Since the first cases were seen at the end of December 2019, coronavirus has spread from Wuhan, China, to 34 countries around the world, with more than 80,000 cases recorded. A hospital was built in 10 days to provide the 1,000 beds needed for those who had fallen victim to the virus in Wuhan 97 per cent of cases reported are in China.

The World Health Organisation (WHO) has said the world should prepare for a global coronavirus pandemic. The virus can be spread from person to person via respiratory droplets expelled when an infected person coughs or sneezes. According to the WHO: "Common signs of infection include respiratory symptoms, fever, cough, shortness of breath and breathing difficulties. In more severe cases, infection can cause pneumonia, severe acute respiratory syndrome, kidney failure, and even death."

AI developers have suggested that the technology could have been used to flag irregular symptoms before clinicians realise there is a developing problem. AI could alert medical institutions to spikes in the number of people suffering from the same symptoms, giving them two to four weeks' advance warning which in turn could allow them time to test for a cure and keep the public better informed.

As the virus continues to spread, AI is now being used to help predict where in the world it will strike next. The technology sifts through news stories and air traffic information, in order to detect and monitor the spread of the virus.

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Could artificial intelligence have predicted the COVID-19 coronavirus? - Euronews

The upcoming Newsrewired conference, taking place on 4 June at MediaCityUK, will feature a workshop where delegates will learn how to start implementing artificial intelligence (AI) in their everyday journalistic work.

The session will be led by Charlie Beckett, a professor in the Department of Media and Communications and founding director of Polis, the London School of Economics international journalism think-tank.

Professor Beckett is the author of the study "New powers, new responsibilities. A global survey of journalism and artificial intelligence". He said that newsrooms have between two and five years to develop a meaningful strategy or risk falling behind their competitors.

"This is a marathon, not a sprint but theyve got to start running now.

"Youve got two years to start running and at least working out your route and if youre not active within five years, youre going to lose the window of opportunity. If you miss that, youll be too late," he said in an article for Journalism.co.uk.

Its really clear if you look at other industries that AI is shaping customer behaviour. People expect personalisation, be that in retail or housing, for production, supply or content creation. They use AI because of the efficiencies that it generates and how it enhances the services or products it offers.

Charlie Beckett is currently leading the Polis Journalism and AI project. He was director of the LSEs Truth, Trust and Technology Commission that reported on the misinformation crisis in 2018.

He is the author of "SuperMedia" (Wiley Blackwell, 2008) that set out how journalism is being transformed by technological and other changes. His second book "WikiLeaks: News In The Networked Era" (Polity 2012) described the history and significance of WikiLeaks and the wider context of new kinds of disruptive online journalism.

He was an award-winning filmmaker and editor at LWT, BBC and ITN.

To take advantage of our early-bird offer, book your ticket before 28 Fabruary 2020 and save 50.

If you like our news and feature articles, you can sign up to receive our free daily (Mon-Fri) email newsletter (mobile friendly).

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Learn how to start using artificial intelligence in your newsroom (before it is too late) - Journalism.co.uk

With advanced research happening in the realm of artificial intelligence (AI), the technology is poised to become smarter than its human creators. But until that day, it is like to harbour sexist, racist and even homophobic tendencies all inherited from its makers social and cultural biases.

This was discussed at some length last year at Rising, one of the countrys biggest gatherings of women trailblazers in the fields of data science and AI. Held on March 8 to commemorate Womens Day, the one-day event hosted more than 250 participants and featured more than 15 sessions led by industry leaders, mostly women.

One of the speakers on the occasion, Director of Citi Saraswathi Ramachandra, provoked a discussion around a hotly debated topic Is AI sexist. According to her, this cannot be firmly answered in the affirmative since AI models can only respond to what it has learned. This means that the real culprit is essentially the training dataset we feed it, and not the technology by itself.

At the heart of it, AI enables tasks to be automated without dependence on step-by-step assistance by humans. How does this work? If a computer is fed enough examples relevant to a given task, it can use ML algorithms to draw an inference. It then finds a way to automatically optimise this approach and goes on to essentially, teaches itself.

To sum it up, AI software trains itself using data that is manually delivered by humans. This means that as it stands now, some level of subjectivity in the outcome cannot be avoided. What is more, as this technology develops, it continues to subtly imbibe these biases from sources like articles and webpages. Thus, our prejudices rub off on technology, thereby reinforcing and exaggerating common stereotypes.

Ramachandra illustrates this with an example.

With chatbots becoming popular across websites and social networks, Microsoft launched its Twitter chatbot Tay in March 2016. However, it was taken down within 24 hours. Tay was designed to mimic a millennial and engage in human conversations with its users. Built on the principles of AI, it was programmed to learn from these interactions and get better at it.

But cheeky Twitter users targeted her vulnerabilities and manipulated her into making deeply sexist and racist statements.

Specifically taking one conversation, Ramachandra spoke about the degree of bias that had been absorbed in the chatbot in less than a day after its launch. The comment by a user went like this We must secure the existence of our people and future for white children. To this, Tay responded by saying that it couldnt agree more. I wish there were more people talking about these things.

According to her, this example clearly demonstrates how quickly machines amplify any biases we may have. This shows that our prejudices play a big role in shaping AI as we know it, potentially becoming even more dangerous as these seep into programs and algorithms.

We often mentally categorise certain jobs on the basis of gender. While homemakers and nurses are more oriented towards women, we have internalised that engineers and doctors are the mainstays of men. This can only be explained away by our innate biases.

This does not portend well for AI systems, that are essentially inheriting these biases and amplifying it with self-learning techniques. So if the fundamental problem rests in the biased datasets we are feeding AI systems, what are the corrective measures that we can take?

A good place to start would be to increase the participation of women in STEM so that they can take up jobs for programming these very AI systems. This becomes imperative because if careful changes are not made to technologies that augment misperceptions about women and marginalised races, AI will continue to proliferate these stereotypes.

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Is artificial intelligence Sexist? The answer is Yes And No - Analytics India Magazine

The White Houses recent efforts to chart a national artificial intelligence (AI) policy are welcome and, frankly, overdue. Funding for AI research and updating agency IT systems is a good start. So is guidance for agencies as they begin to regulate industry use of AI. But theres a glaring gap: The White House has been silent about the rules that apply when agencies use AI to perform critical governance tasks.

This matters because, of all the ways AI is transforming our world, some of the most worrying come at the intersection of AI and the awesome power of the state. AI drives the facial recognition police use to surveil citizens. It enables the autonomous weapons changing warfare. And it powers the tools judges use to make life-changing bail, sentencing and parole decisions. Concerns about each have fueled debate and, as to facial recognition in particular, new laws banning use.

Sitting just beyond the headlines, however, is a little-known fact: AI use already is pervasive in government. Prohibition for most uses is not an option, or at least not a wise one. Needed instead is a frank conversation about how to give the government the resources it needs to develop high-quality and fairly deployed AI tools and build sensible accountability mechanisms around their use.

We know because we led a team of lawyers and computer scientists at Stanford and New York universities to advise federal agencies on how to develop and oversee their new algorithmic toolkit.

Our research shows that AI use spans government. By our estimates, half of major federal agencies have experimented with AI. Among the 160 AI uses we found, some such as facial recognition are fueling public outcries. But many others fly under the radar. The Securities and Exchange Commission (SEC) uses AI to flag insider trading; the Centers for Medicare and Medicaid Services uses it to ferret out health care fraud. The Social Security Administration is piloting AI tools to help decide who gets disability benefits, and the Patent and Trademark Office to decide who gets patent protection.

Still other agencies are developing AI tools to communicate with the public, by sifting millions of consumer complaints or using chatbots to field questions from welfare beneficiaries, asylum seekers and taxpayers.

Our research also highlights AIs potential to make government work better and at lower cost. AI tools that help administrative judges spot errors in draft decisions can shrink backlogs that leave some veterans waiting years (sometimes, close to a decade) for benefits. AI can help ensure that the decision to launch a potentially ruinous enforcement action does not reflect the mistakes, biases, or whims of human prosecutors. And AI can help make more precise judgments about which drugs threaten public health.

But the picture is not all rosy.

First, the government has a long way to go. Our teams computer scientists found that few agency AI uses rival the sophistication found in the private sector, making it harder to realize accuracy and efficiency gains. Some may wish to keep agencies low-tech to limit surveillance or otherwise hamstring government. Its not that simple: Government use of makeshift and insecure AI systems puts everyone at risk. Disabled persons, veterans and all of us deserve better.

Second, AI poses deep accountability challenges. When public officials make decisions affecting rights, the law generally requires an explanation. This reason-giving requirement is deeply embedded in law and even enshrined in the Constitution. Yet sophisticated AI tools are opaque; they do not serve up explanations with their outputs. A crucial challenge is how to subject these tools to meaningful accountability and ensure fidelity to longstanding commitments to transparency, reason-giving and non-discrimination.

To address concerns, agencies could be required to politically ventilate AI tools the way they must new regulations. Or they could be made to benchmark AI tools, reserving a pool of cases for human decision and comparing results to AI-assisted ones. However, there are no one-size-fits-all solutions. Open-sourcing computer code might make sense when agencies distribute welfare benefits. But disclosing details when tax enforcers use AI to identify cheaters will just aid evasion.

Third, if we want agencies to make responsible use of AI, their capacity must come from within. Our research shows that many of the best-designed AI tools were created by innovative, public-spirited agency technologists not profit-driven private contractors. The AI tools that help adjudicate disability benefits at the Social Security Administration came from agency insiders with intimate knowledge of governing law and how administrative judges work.

This makes sense. Government work is often complex. Recruiting skilled technologists and updating outmoded computing systems is crucial to building high-quality AI tools and administering them fairly. But it wont be cheap.

Last, AI can fuel political anxieties. Government AI use creates a risk of gaming by better-heeled groups with resources and knowhow. The SECs algorithmic predictions may fall more heavily on smaller companies that, unlike big Wall Street players, lack a stable of quants who can reverse-engineer the model and keep out of the agencys cross-hairs. If citizens come to believe AI systems are rigged, political support for a more effective, tech-savvy government will evaporate.

In short, this is a pivotal moment for government. Managed well, agency AI use can make the government more efficient, accurate and fair. Managed poorly, AI can widen the public-private technology gap, make agencies more vulnerable and less transparent, and heighten concerns about government arbitrariness and biases that are coursing through American politics.

Wherever the nation lands on facial recognition, government AI use is here to stay. The question now is which of these two visions becomes reality.

David F. Engstrom and Daniel E. Ho are professors of law at Stanford University. Catherine M. Sharkey is a professor of law at New York University. Mariano-Florentino Cullar is a justice on the California Supreme Court and professor of law at Stanford University.

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What to do about artificially intelligent government | TheHill - The Hill