The rise of Artificial Intelligence & its place within the UAE’s industrial revolution strategy
By World Healthcare Journal-
It is very difficult to identify the needs of a country that seemingly has it all. The vision set out by the UAE government in its Fourth Industrial Revolution Strategy is “to become a leading global hub and an open lab for the fourth industrial revolution’s applications”. But the question is – what does that mean, and how can they get there?
In 2015, the UAE government injected 300bn AED in order to foster a knowledge economy within the country. The UAE itself is also the only country with a standalone Ministry for Artificial Intelligence or AI, and its ambitious AI strategy looks to place the UAE at the forefront of global efforts to develop artificial intelligence. They currently envision a global market value of a monumental 15.7 trillion by 2031.
The AI strategy has eight objectives; including reaffirming the UAE’s position as a global AI hub, employing AI in customer services, and recruiting and training people to work in fields which will be driven by the technology for years to come. This strategy covers the development and application of advanced technologies in nine sectors; encompassing transport, health, space, renewable energy, water, technology, education, environment, and traffic.
A paper commissioned by Microsoft and conducted by EY, reported in the press in June 2019, says the UAE has seen the second-highest AI investment over the past decade, more than $2.15 bn.
- One in five companies in the country consider AI as their top digital priority.
- 94 per cent of C-suite leadership consider ‘AI strategy’ as an important topic and 35 per cent of nonmanagerial staff are actively having AI discussions.
Social, Mobility, Analytics, Cloud
Being digital or not is no longer a discriminator as the common denominator is expected to be digital. The new discriminator in technology terms is how much of a SMAC the organisation is. Social, Mobility, Analytics, Cloud (or SMAC for short) is the new enterprise IT model that is disrupting the world.
Deloitte reports that, despite experts’ agreement about the value of using social, mobile, analytics and cloud technologies, the health care industry lags behind others in digital technology adoption.
Artificial Intelligence has a major role in any SMAC model, with particular functionality in the analytics section of the model. According to a report published by Deloitte in 2015, Healthcare as an industry was further down in its digital maturity. It will be interesting to see how it ranks with the investment and focus it has received in recent years.
Artificial Intelligence and its vision
AI is simplistically described as the collection of data, and is used to predict outcomes in the short and long term. According to Forbes, Artificial Intelligence is probably the most complex and astounding creation of humanity yet.
As AI research intends to make machines emulate human-like functioning, the degree to which an AI system can replicate human capabilities is used as the criterion for determining the types of AI. These include reactive machines or machines with limited memories which are best used for repetitive tasks.
Another criterion is the emotional intelligence capability based upon the Theory of Mind. This AI will be able to better understand the entities it is interacting with by understanding their needs, emotions, beliefs and thought processes.
The penultimate form of AI will be the ‘self-aware AI’, which will be evolved to be so akin to the human brain that it will develop self-awareness. Creating this type of AI is and will always be the ultimate objective of all AI research. This type of AI will not only be able to understand and evoke emotions in those it interacts with, but also have emotions, needs, beliefs, and potentially desires of its own. Needless to say, the complexity of ethical and legal challenges that will come along with the use of these innovations is immense.
The most common type of sub-classification of AI divides AI into three groups:
- Artificial Narrow Intelligence (ANI): Artificial narrow intelligence refers to AI systems that can only perform a specific task autonomously using human-like capabilities.
- Artificial General Intelligence (AGI): Artificial General Intelligence is the ability of an AI agent to learn, perceive, understand, and function completely like a human being. These systems will be able to independently build multiple competencies and form connections and generalisations across domains, massively cutting down on time needed for training. This will make AI systems just as capable as humans by replicating our multifunctional capabilities.
- Artificial Super Intelligence (ASI): The development of Artificial Superintelligence will probably mark the pinnacle of AI research, as AGI will become by far the most capable forms of intelligence on earth. ASI, in addition to replicating the multifaceted intelligence of human beings, will be exceedingly better at everything they do because of overwhelmingly greater memory, faster data processing and analysis, and decision-making capabilities.
That is not all. There is another aspect of AI competency being built in the future AI models, providing reasoning for its decisions or Explainable AI. For example, AI-enabled sensors examining a patient suggest an AI-driven decision for urgent action. This will not be enough on its own. Clinicians will need to know the reasons and rationale behind it. In other words, the AI has to “explain” itself, by opening up its reasoning to human scrutiny.
Focusing on Futuristic Healthcare
Healthcare is a lucrative market for the application of these technologies. According to a research report by the market research and strategy consulting firm, Global Market Insights Inc, the Healthcare Information Technology (IT) Market is predicted to be worth over $441.8bn by 2025. There are many exciting potentials for innovation in healthcare described in the AI strategy with the use of augmented reality, robotics, connected services, so SMART services can play a leading role in the facilitation of the delivery of healthcare amongst other industries.
The UAE, and Dubai in particular, already have many state-of-the-art health care organisations which are private-public partnerships as well as totally government-run. However, the 4IR strategy aims to encompass much more in its futuristic healthcare model. To deliver these through an externally commissioned sector is only a short-term solution. The sustainability of the strategy relies on homegrown capacity, competency, and values and ethics of emerging technologies awareness.
In order to become self-sustained in the near and distant future to deliver this aspirational strategy, the fundamental goals are to do with capacity and competency building, and understanding the values and ethical challenges which come with such technologies. The strategy includes the following two aspirations:
- Future Talent: Prepare a national talent pool and entrepreneurs for the Fourth Industrial Revolution and equip them with the required knowledge and skills in advanced science and technology through an applied educational system focused on innovation and entrepreneurship in the high priority sectors.
- Values and Ethics: Cultivate steadfast values and ethics in the future generations to ensure making the optimal use of the Fourth Industrial Revolution and steadily facing its challenges.
Technical skills have been the focus of hiring in years past, but these skills have rapidly declining shelf lives. The rise of AI and automation means employees are increasingly tasked with jobs that only humans can do, such as thinking creatively, using judgment and employing empathy. Gone are the days when one qualified as a professional in an industry and retired in the same - now individuals have to adapt and reinvent themselves several times to remain at the top of the job market. Companies, as well as education systems, will need to shift how they assess and train people accordingly.
In healthcare, there is a new focus to review the traditional curriculum and emphasise a change adaptability syllabus to train the future workforce in an ability to be lifelong learners and change acceptors. There will be a need to create innovative roles such as data ethicists and knowledge mechanics to enable seamless implementation of the visionary AI and 4IR strategy.
Values and ethics preparation
The difference between previous introductions of new technologies and current times is the speed with which change is occurring. Disruptive and sustainable innovations are overwhelmingly fast in their development and introduction to markets. While these technologies are used in process management to streamline healthcare-related management tasks, there is no real ethical dilemma.
However, when it comes to the use of these technologies in making clinical decisions and data handling, there are some serious discussions to be had on the ethical standing for a clinician who is going to use them in their clinical managements. In medicine, the basic ethical principle is ‘Nonmaleficence’ i.e. first do no harm. Any intervention that is made in clinical practice must have a sound scientific credibility, clinical validity and reliability of predicted outcomes. In the biopharma industry, the period it takes to develop a new drug and go through clinical trials is around 10 to 15 years on average. Medical devices have classifications as to the risk stratification of their uses and have to go through rigorous regulatory scrutiny.
On 2 April 2019, the FDA published a discussion paper “Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device. Here is where AI Explained will come in, as the inertia in clinical uptake is due to this non-traditional decision-making that is reliant on algorithms and machine learning rather than a clinician at the end. This mindset will only be reassuringly satisfied when there is clarity as to how the machine came to that decision.
The challenge in taking a stance with a decision or solution made by an emerging technology is that there is no historical data to assess its safety profile. The ethics of emerging technology, therefore, has to deal with an epistemological problem - the problem of uncertainty concerning future devices, applications, uses and social consequences.
There are two approaches to come to an ethical framework for these. One is the generic approach which is a restrictive approach of wait and watch for the technology to develop and with time show its full spectrum of outcomes. The other is the forecasting approach to the ethics of emerging technology. The forecasting approach relies on predictive studies of future technological devices, uses and social consequences.
These are issues that face not only the UAE but all countries who plan on using digital and E-health solutions, not only for immediate clinical uptake but for longer-term integrated healthcare systems.
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