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Projected a few years ago to be a $150 billion industry by 2026, Artificial Intelligence (AI) systems are radically transforming industries around the world and healthcare is no exception to this development. New AI applications are being developed and experimented with to streamline administrative and medical processes, enhance clinical decision making and support, manage long-term care - all of which are showing great promise.

AI in healthcare refers to the use of complex algorithms designed to mimic human cognition and perform certain tasks in an automated fashion at a fraction of the time and cost. Simply put, when data is injected into the platform, algorithms, and machine learning solutions kick in, working with the data, using deep data analytics, and delivering outcomes and reports which would be as accurate if not more than human interventions.

From making more accurate diagnoses, finding links between genetic codes to powering surgical robots, maximising administrative efficiency, and understanding how patients will respond to treatment plans, there are limitless opportunities to leverage AI in healthcare.

Using machine learning in precision medicine can help predict what treatment protocols are likely to succeed based on a patients attributes, treatment history, and context, allowing more accurate and impactful interventions at the right moment in a patients care.

Similarly, the use of voice-activated Electronic Medical Records (EMRs) can go a long way towards optimising a doctors efficiency by reducing hours spent on clerical work and administration.

How AI is being used today in healthcare

Current use cases are already exhibiting AIs transformational impact in healthcare and future potential uses offer astonishing possibilities.

Here are some broad use case scenarios for current AI use:

Improving Diagnostics: It is one of AI's most exciting healthcare applications. AI solutions are helping automate image analysis and diagnosis, removing the possibility of human error in readings.

Drug Discovery: AI is being harnessed to identify new therapies from vast databases of information on existing medicines. This could help improve lengthy timelines and processes tied to discovering and taking drugs.

Predictive Patient Risk Identification: At-risk patients can be swiftly identified by algorithmic analysis of vast amounts of historic patient data. Cohesive health ecosystems that help organize and maintain patient records can play a vital role. This will also help with reducing cost and time in manual drudgery of procedures and optimising healthcare resources.

Primary Care: Direct-to-patient solutions via voice or chat-based interaction are helping provide quick, scalable access for basic medical issues. AI-based voice-to-text technologies save countless hours taken to type memos. The doctor and the patient can speak freely while a voice-enabled assistant listens in and puts down the text into EMRs, streamlining the drudgery of manually scribing patient history and easing out the problem of missing medical records.

AI Robot-Assisted Surgery: It is another area that is being explored to help with everything from minimally-invasive procedures to open-heart surgery. Working with doctors, robots have already been able to carry out complex procedures successfully with precision, flexibility, and control that goes beyond human capabilities.

Challenges

The use of AI is certainly surging in healthcare, however, it is still early days, and adoption of AI in healthcare is not without challenges that may impede its momentum.

For any AI solution to be successful, it requires a vast amount of patient data. Getting access to private medical records, however, poses the all-important issues of data privacy and ethics. Privacy is expected and enforced especially strongly when it comes to private medical data. There is room for some work around protecting patient data privacy and the answers may lie in cohesive healthcare ecosystems that will have to weave in cybersecurity as an essential component of their world view.

Regulationthis is another challenge with additional geolocation implications. Different nations will adopt different guidelines around levels of transparency in automated decision-making. Informed consent also poses questions especially when participating individuals in some cases may not be physically or mentally equipped to give consent.

Although hard to establish its parameters, transparency is vital to medical AI. A doctor needs to be able to understand and explain why an algorithm recommends a procedure or line of treatment at least until the machine itself learns to come up with more intuitive and transparent prediction-explanation tools.

Quality and usability of data is also a challenge because health data can be subjective, fragmented, and often inaccurate. While the subjectivity issue may need a cultural change, the fragmentation in legacy data can be rectified at an ecosystem level, wherein different stakeholders with access to data ingest it into a central repository.

User adoption at both patient and practitioner ends is another significant challenge. Doctors decisions are based on training, experience, and intuition, as well as problem-solving skills. For doctors to consider suggestions from machines can be difficult. Similarly, the human touch of interacting with a doctor can be lost with these types of tools. Patients may be reluctant to trust a diagnosis from an algorithm rather than humans.

Future outlook of AI

Evolving healthcare ecosystems will have to balance the use and perception of AI for both clinicians as well as patients. They must develop and use AI in hybrid models. It should be seen as an aid or amplifier of medical knowledge and not as a replacement for doctors. AI should be used and perceived as supporting diagnosis, treatment planning, and identifying risk factors, but clinicians retain final charge for a patients care. The hybrid model will help in accelerating the adoption of AI by healthcare practitioners while delivering measurable and scalable improvements in health outcomes.

Artificial intelligence is certainly pushing the envelope towards making game-changing improvements in healthcare. While efforts and advances need to be made before AI solutions can be deployed in a safe and ethical way, AI does open up limitless possibilities to accelerate the move of healthcare into a seamless ecosystem-based model that promises to drive improvements across the care continuum.

This article has been written by Aneesh Nair, Co-Founder and CIO, MyHealthcare

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How artificial intelligence is transforming the future of healthcare one step at a time - HT Tech

The world around us is changing rapidly. With the arrival of industrial revolution 4.0, businesses of all sizes and types are increasingly capitalizing on advanced, intelligent technologies. They are taking advantage of automation to reduce time-consuming, tedious tasks, especially automating assembly line work. However, as such intelligent technologies are better performing than humans, it is necessary businesses must think of knowledge management for their employees. Knowledge is significantly a crucial aspect in achieving high-quality performance for employees. The field of knowledge management consists of psychology, epistemology, and cognitive science. Gaining information provided by artificial intelligence can play a crucial role in helping business employees make timely decisions.

Knowledge grows when used and deflates when kept under lock. Thats true! Artificial intelligence provides the mechanisms that enable machines to learn. It allows them to gain, process and utilize knowledge to perform tasks. AI also enables machines to unlock knowledge that can be delivered to humans to improve the decision-making process.

Artificial intelligence has a crucial role to play in modern businesses. Every year, a fresh trove of companies emerge implementing AI-driven solutions across business processes. Many executives believe employing AI in their businesses will help both people and machines, and enable them to work together to improve operations. Furthermore, reports indicate that the increasing development and adoption of AI will boost the global GDP by up to 14% by 2030.

AI allows people in organizations to make effective business decisions, ensure customer loyalty and avert expensive production downtime. It does so as advanced forms of AI are programmed into insight engines and knowledge management systems. Insight engines emerged recently from the world of search, work with data stored in multiple silos within an organization and connect them together to populate answers in search results.

Insight engines work as an intelligent solution that makes information to be found resource-efficient and available to the user in the right context for their respective business case. These systems are equipped with artificial intelligence that helps obtain and glean existing corporate knowledge, excerpt the information, and show correlations between the individual pieces of data to provide a comprehensive overall picture. With the help of natural language processing (NLP) and natural language question answering (NLQA), insight engines can deliver search queries in more innate language and be processed directly. These intelligent solutions assess and interpret structured metadata and text content and use this to accurately determine what the user needs. Most knowledge management systems use these technologies along with the semantic processing of content that enables natural human-machine interaction.

Most companies around the world perceive knowledge management as an IT project. They try to convey information from one place to another. Nonetheless, knowledge management is more about comprehending the resource and getting aware of how to leverage it for business growth. It is like an intellectual asset for the business.

In his book, The Fifth Discipline, Peter Senge, a lecturer at the MIT Sloan School of Management, points out that learning organizations are always intensifying their knowledge, finding new ways of creating knowledge, moving it seamlessly throughout the organization, and transforming it so that people have insights into what they need to do. This requires a knowledge infrastructure involving numerous components such as databases, internal experts, libraries, research centers, outside information agents, and other knowledge-based sources for filling the knowledge gaps within the organization. There is also a need to measure and manage the value of knowledge so that it fits efficiently within the organization. Many companies have positions like Chief Knowledge Officers or Chief Learning Officers to help drive this process.

Above all, the new developments in intelligent systems will enable businesses to make effective use of enterprise search and knowledge management. Employees can use artificial intelligence that allows them to unlock knowledge to improve the decision-making process and generate high ROI.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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Unleashing the Power of Artificial Intelligence to Leverage Knowledge - Analytics Insight

Congress stood-up the National Security Commission on Artificial Intelligence (NSCAI) to make recommendations to the President and Congress to advance the development of artificial intelligence [AI], machine learning, and associated technologies to comprehensively address the national security and defense needs of the United States. The 2019National Defense Authorization Act (NDAA), Section 1051further instructed the NSCAI to focus on issues including global competition, research and development, risks and ethical concerns.

NSCAI published interim reports in 2019 and 2020 and the executive branch and Congress adopted some of the reports recommendations in the William M. (Mac) ThornberryNDAAfor Fiscal Year 2021. The recently published 756-pageNSCAI Final Reportcomprehensively focuses on defense and the future technological standing of America, and includes precise recommendations. The NSCAI includes 15 commissioners, nominated by Congress and the executive branch, who represent a diverse group of technologists, business executives, academic leaders and national security professionals. The group was led by Chairman Eric Schmidt, the former CEO of Google, and Vice Chairman Robert Work, the former Deputy Secretary of Defense.

The primary conclusion is that America is not prepared to defend or compete in the AI era, but the Commission sets out an integrated strategic plan for partnerships between the government, academia, industry and Americas allies. The Report finds that [e]ven large tech firms cannot be expected to compete with the resources of China or make the big investments the U.S. will need to stay ahead. We will need a hybrid approach meshing government and private-sector efforts to win the technology competition.

Many recommended goals are targeted for completion by year-end through 2025, including AI readiness. The recommendations include a proposed budget of $40 billion for government spending, described as a modest down payment on future breakthroughs. Some of the key areas covered in the Report are:

The Report includes a breakout of funding recommendations by Cabinet departments, major agencies and program offices. Proposed recipients include DoD, DARPA, Department of Homeland Security, National Institute of Standards and Technology, and the Department of Health and Human Services. This is instructive for government contractors and partners who are interested in participating in this work.

McGuireWoods will continue to monitor these important recommendations as they are considered by the President and Congress. Our team is available to assist clients in navigating these comprehensive and leading-edge technologies and industries. In addition, new opportunities for contracts and grants in the areas of artificial intelligence, cybersecurity and national security are posted onbeta.sam.gov, as well as agency websites.

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NSCAI Recommends $40 Billion Investment in Artificial Intelligence, R&D and Innovation - JD Supra

The Internet of Things and Artificial Intelligence are the two most emerging technologies in the world right now. As researchers are expanding the potential of these two technologies, more and more application areas have started to use them. Recently the three emerging technologies the Internet of Things is powered by are Artificial intelligence, 5G, and Big Data.

Artificial intelligence consists of functions that help devices to learn and process information just like humans. 5G networks are mobile networks with greater speed and smooth real-time data processing. While Big Data means the management of the enormous volume of data which are processes from various resources (internet-connected).

Among all these, the technology which is getting major attention is the blend of Artificial intelligence with IoT, popularly known as AIoT. It is something that is holding a promise of a more connected future. As AIoT technology is becoming known to common people, it is transforming the fundamental ways of living the life and processing of data in regular work.

Below 4 mentioned areas will be changed properly because of AIoT. AI and the Internet of Things together mean both intelligence and data. This combination can change everything, there are more possibilities but here we will discuss these 4.

1. Wearables:

Wearable devices like smartwatches are getting better every day. The reason is that AI and IoT app development companies are improving. If we talk about just the smartwatches, you will see how the data and intelligence powers them. Smartwatches work by tracking the activity of the users and are connected directly to the smartphones. AI uses the data that is transmitted by IoT devices and gives results to the users according to their requirements. The market for these devices is growing and it is expected that it will become more than $87 billion by the year 2023.

2. Smart City:

People are moving from villages to cities, cities are also getting smarter and safer. Cities are becoming more and more convenient. Cities are getting smarter at a great pace. Every application development company is also working to analyze the geographical location as precisely as possible. With IoT and AI, traffic management, resource management, and many other things will become automated. This will take a load off the government and they will be able to make better policies. Smart cities will also have better delivery systems. Everything will be mapped to perfection and the applications will be smarter. Deliveries can also be automated if things keep moving at the same pace.

3. Smart Industries:

Industries need to become smarter and AIoT. They are prone to errors and when they will start using AIoT, the chances of human errors will be really less. This will improve their efficiency and will improve the quality of the product of an IoT app development company as well. It is forecasted that more than 80% of IoT projects will be using AI by the year 2022. All industries from mining to manufacturing are relying on digital transformation. Now, they are also trying to move away from manual production. It takes a lot of their money and time.

4. Smart Home:

Everyone wants their home to be smart. With AIoT people would be able to control everything in their house with just a removal or their voice. There are devices that can be connected to the electric circuits of the house and can operate everything with voice commands. The devices will have sensors that will make them smart enough that they will turn on or off by just observing the environment. It is expected that the smart home market will see an annual growth of 25% during 2020-2025 and will reach $246 billion. This is a huge number but the way things are escalating, it is possible. Also, no matter how expensive, people are spending money on making their houses smart. This is something that provides them extra comfort and also makes them feel like they are in control. The smart home is just the reality that people used to see in Hollywood fiction movies a few years ago.

AIoT has the ability to test the amount of data a device can process and its future advancements. This will open the door to processing and learning.

Through the technology of edge computing, the data processing can be done in the computer itself without sending it to remote data centers. While the current technology is limited to smart thermostats and appliances, the future may hold some really advanced gadgets like home robots and autonomous fully-functioning vehicles.

Another major advancement is the improvement of voice AI in devices like speakers and mobile phones. In todays era, there are 1D smart speakers that obey the commands of the person who is speaking. It can be replaced with speakers carrying the Natural Language Processing technique to understand the user in a better way. There are 2D Voice-activated LCDs that help in displaying information. An IoT development company always strives to make businesses seamless through technology. With the successful implementation of AIoT, the beginning of ePayment voice authentication now seems possible in the next few years.

The application of Vision AI is now known to everyone. This is a pure AI device used for the detection of massive objects in 4K resolution. When combined with the internet of things, the vision AI might be able to analyze video on the edge. The quality of the display is also predicted to increase to 8K instead of 4K.

AIoT is all set to take a massive turn in the market. With its successful implementation, it can be incurred into various industries such as healthcare, real estate, e-commerce, and whatnot. It is important to secure this technology as much as possible to prevent it from getting misused. Every IoT App development company is working on this project for the past few years. In the next five to ten years, AIoT will completely change the way people will use technology.

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Artificial Intelligence And The Internet Of Things (AIoT) Hold The Promise Of A More Connected Future - Customer Think

Artificial Intelligence

One of the initial hesitations in many enterprise organizations moving into the cloud in the last decade was the question of security. Significant amounts of money had been put into corporate firewalls, and now technology companies were suggesting corporate data reside outside that security barrier. Early questions were addressed, and information began to move into the cloud. However, nothing stands still, and the extra volume of data and networking intersects with the increased complexity of attacks, and artificial intelligence (AI) is being used to keep things safe.

The initial hesitation for enterprise organizations to move to the cloud was met by data centers improving hardware and networking security, while the cloud software providers, both cloud hosts and application providers, increased software security past what was initially offered in the cloud. Much of that was taking knowledge from on-premises security and scaling it to the larger systems in the cloud. However, theres also more flexibility for attacks in the cloud, so new techniques had to be added. In addition, most organizations are in a hybrid ecosystem, so the on-premises and cloud security must coordinate.

This means an opportunity for AI to provide enhanced security. As mentioned with other machine solutions, security is a mix different AI and non-AI techniques to fit the problem. For instance, theres deep learning. Supervised learning can be used for known attacks, while unsupervised learning can be used to detect anomalous events in a sparse dataset. Reinforcement learning classification can even be done with statistical analysis in time series, and not always require AI. That can provide faster performance in appropriate cases.

On a quick tangent, lets talk about supervised learning and reinforcement learning. Some folks present them as different; I think of the latter as an extension of the former. Classic supervised learning is when input is labeled and the labels are important for the AI system, as they are used to understand and organize the data. When there are errors, humans add more annotations and labels to existing data, or they add more data. In reinforcement learning, feedback for the neural network is given as to how far the results of an iteration are from a set goal. That feedback can be put back into the system by programmers changing weights or, in more advanced systems, by the AI software doing the comparison and adapting on its own. That is a type of supervision, but Ill admit its a philosophical argument.

Back on track, lets add another complexity. In the early days of the cloud, applications were larger but still followed a similar pattern of scale-up and scale-out. Now theres something changing both environments: containers. Simply put, a container is a piece of software that wraps around an application, it has basic services and even a virtual operating system. That allows containers to run on multiple operating systems regardless of internal application code. It also allows cloud platforms and servers to more finely control services to their clients in order to meet service level agreements (SLAs) that provide quality performance to the end customer.

As more applications migrate to a container architecture, its important for security to keep up, said Tanuj Gulati, CTO, Securonix. Light weight collectors can run within application containers, such as with Docker, collecting and sending relevant event logs to the more robust security monitoringapplications running separately. This provides strong security in the new environments without significant burden being added to application performance.

In my discussion with Tanuj Gulati, he explained that they first worked in the virtual machine (VM) environment in local data centers. That provided both an understand that helped extend security to Docker, but also in integrating security between on-premises and cloud systems in a hybrid environment.

Artificial intelligence is focused on detection, but a complete system must also address the response to a perceived threat. The basic system can detect attacks, and based on known problems rules can then determine responses. Unknown problems have unknown responses. Humans must be flagged to handle those questionable transactions, then feedback can be given to reinforce the system. Depending on how complex a system is created, those new rules can be incorporated into the neural network or added to a rules set.

The state of the industry, both in technology and human comfort levels, shows that there will continue to be human oversight before responses to new attacks as the predominant method in the next few years. Advances will push the security industry into more system action and then reporting, review, and adjustment by humans, but that will happen slowly. What will help is that better explainability will be required, as the deep learning black box will have to become more transparent.

Cloud computing and artificial intelligence are growing in parallel. The complexity of the cloud is driving the need for AI, but the complexity of AI is also creating the need for it to work better in the cloud environment with efficiency, transparency and control.

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Security In The Cloud Is Enhanced By Artificial Intelligence - Forbes