Other neural networks
Other work on neuron like computing includes the following:
Visual perception.
Networks can recognize faces and other objects from visual data. A neural network designed by John Hummel and Irving Biederman at the University of Minnesota can identify about 10 objects from simple line drawings. The network is able to recognize the objects—which include a mug and a frying pan—even when they are drawn from different angles. Networks investigated by Tomaso Poggio of MIT are able to recognize bent-wire shapes drawn from different angles, faces photographed from different angles and showing different expressions, and objects from cartoon drawings with gray-scale shading indicating depth and orientation.
Language processing.
Neural networks are able to convert handwritten and typewritten material to electronic text. The U.S. The Internal Revenue Service has commissioned a neuron like system that will automatically read tax returns and correspondence. Neural networks also convert speech to printed text and printed text to speech.
Financial analysis.
Neural networks are being used increasingly for loan risk assessment, real estate valuation, bankruptcy prediction, share price prediction, and other business applications.
Medicine.
Medical applications include detecting lung nodules and heart arrhythmias and predicting adverse drug reactions.
Telecommunications.
Telecommunications applications of neural networks include control of telephone switching networks and echo cancellation in modems and on satellite links.
AI AT PRESENT.
In today’s Times artificial intelligence has helped us in improving our life cycle , providing the dynamics and value proposition to it.
AI already is helping us transform or re-produce the body organs we are in need of.
With the help of artificial intelligence we have successfully managed to overcome or grow above and beyond diseases , medical experiments and improving our life and harnessing new ways to co-exist.
Current uses of AI in healthcare and medicine.
We have already seen many major breakthroughs in the health and medicine sectors there are some important developments and advancement because of artificial intelligence in the industry.
Medicine.
Ensuring reliable lab results
Modern medicine is practiced just as much in the laboratory as on a hospital ward. Laboratory tests of human samples – such as for identifying and measuring viruses, bacteria and microscopic components of the human body – are essential for determining a diagnosis, selecting the best course of treatment and dispensing the right dose of medicine. The quality of a hospital or doctor’s office is therefore highly dependent of the quality of its laboratory. But how can one ensure that methods and equipment yield reliable results?
Laboratory quality control is the answer. And it was invented in Norway.
In the 1950s, Professor Lorentz Eldjarn conducted the world’s first successful experiments using a standardised control serum for internal quality control at his laboratory at Oslo University Hospital, Rikshospitalet. Thirteen years later, the company SERO was born, and the world’s first commercially available control serum, Seronorm™, was launched.
SERO is still a pioneer in quality control materials. It has a steadily growing portfolio of standard and tailor-made products that are used around the globe for quality control of lab tests and calibration of equipment. The global market for laboratory quality control is currently some USD 905 million. Thanks to Norwegian research, doctors and researchers worldwide can trust their lab results.
Fighting cancer that has spread to the bones
Some types of cancer are more difficult to treat than others. When cancer has spread to the bones, for example, there are few alternatives. Available treatments have major side-effects and are of varying efficacy. Fortunately, a Norwegian company has found a way to slow bone metastases.
Building on research conducted at Oslo University Hospital, Radiumhospitalet, Algeta has developed a cancer drug based on radium-223, a radioactive isotope. While radium treatment of cancer was widespread in the past, it has more or less been replaced by alternatives with fewer side-effects. Algeta’s breakthrough is a targeted drug that is highly precise and has a short half-life, thereby minimising side-effects. The drug was launched under the name Alpharadin, and is now called Xofigo.
Some 1.3 million men are diagnosed with prostate cancer each year. Xofigo is used to treat prostate cancer when the cancer has spread to the bones, and is approved for use in both Europe and the US. Founded in 1997, Algeta was acquired by the multinational company Bayer in 2011. However, production and research activities are still located in Norway, where work is being done to develop similar methods for treating breast cancer and lung cancer.
Norwegian invention isolates DNA from cells
In 1977, Professor John Ugelstad at the Norwegian University of Science and Technology managed to solve a problem that had been puzzling researchers for years: creating a set of microscopic beads of exactly the same size. The professor and his team then went on to make these uniform beads magnetisable, and found that they could be used to separate biological materials with extremely high precision.
The company Dynal was founded shortly after to further develop and commercialise the technology. The beads were given the name Dynabeads and have since been used in isolating and removing cancer cells, isolating DNA, tissue-typing in connection with organ transplantation, and HIV research.
Dynabeads are still produced in Lillestrøm, near Oslo, and are used in roughly 80 per cent of all oncological sequencing in Europe.
Mapping blood flow in the heart
Cardiovascular disease is the world’s most common cause of death, and the risk increases with age. Cardiac tests are thus some of the most important and fundamental medical procedures there are. When doctors examine whether a heart is beating as it should be, they use ultrasound, which provides a living picture of the heart’s functioning. However, at the end of the 1970s there were no effective methods of obtaining a detailed picture of how the blood flows through the heart.
This was remedied by the development of the world’s first Pulsed Echo Doppler Flowmeter (PEDOF) at the Norwegian University of Science and Technology. GE Vingmed Ultrasound further developed and commercialised the PEDOF machine, advancing ultrasound technology by using the Doppler effect to create a precise picture of where and how fast the blood flows through the heart. This gave doctors a new, more accurate tool for diagnosing disease and irregularities, which is now used to examine roughly 200 000 hearts each day.
Given that the global population is ageing and cardiovascular disease is becoming more prevalent, the demand for GE Vingmed Ultrasound’s technology continues to grow. The technology, too, is steadily evolving. For example, the company recently launched the first pocket-sized ultrasound with two transducers in one probe, giving much greater flexibility in the use of ultrasound.
Pioneering Norwegian professor Bjørn Angelsen invented a Doppler ultrasound device to measure blood flow that would evolve into the modern ultrasound imaging equipment used in tens of millions of procedures each year. He also managed to devise the “E = mc2 of cardiology” along the way.
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The story of Norway’s medical ultrasound industry starts in the early 1970s in Trondheim when a young doctor, Alf Brubakk, who was working at the regional hospital (now St Olav’s Hospital) met an ambitious engineering student, Rune Aaslid, from the Norwegian Institute of Technology (now the Norwegian University of Science and Technology (NTNU)), who claimed he could make a mathematical model of the cardiovascular system. Together, they created Jenny, a patient simulator built using analogue electronics and named after the first real patient their device was used on [1, 2, 3].
As they worked on their model, it became clear to Aaslid and Brubakk that the theory on blood-flow simulations required experimental data. Could ultrasound be a useful means of acquiring such data?
The solution came with Bjørn Angelsen: compelled by the mathematical challenges of the project combined with his intimate knowledge of ultrasound waves, he developed a device called the Pulsed Echo Doppler Flowmeter (PEDOF) that could accurately measure blood flow velocities in the aorta and heart based on the Doppler effect.
Pioneering the “E = mc2 of cardiology”
The 1960s and 70s saw an explosion of new possibilities in cardiac surgery thanks to technological developments and safer anaesthesia.
“Cardiac surgery was the real challenge at that time, everyone in the medical field was focused on that. The cardiologists were focused on invasive techniques to measure pressures in the heart, which means putting in catheters. And so, at the time ultrasound looked a little sort of esoteric,”
“But then along came a doctor who had a very interesting background, and he completely changed this perspective.”
Angelsen
The doctor Angelsen refers to was Jarle Holen, who had previously worked as an aerodynamics engineer on the Boeing supersonic jet project in Seattle in the 1960s. In collaboration with Rune Aaslid, he used his aviation background and knowledge of fluid dynamics to apply Bernoulli’s principle to heart valves. Together, they calculated pressure gradients in patients with narrowed heart valves using estimates of blood flow velocities from the PEDOF device [4]. This made it possible to calculate the pressure inside the heart using harmless ultrasound waves, thus avoiding invasive catheterisations.
Building on this innovation, Angelsen experimented on optimal sensitivity Doppler equipment that could be used in pulsed wave (PW) to obtain spatial resolution inside the heart cavities and in continuous wave (CW) to measure the high velocities that occur in heart valve lesions. Another challenging task he tackled was real-time estimation of the Doppler frequency to give time-variable curves of the blood velocities inside the beating heart.
Angelsen further simplified and introduced material parameters into the Bernoulli equation, resulting in the formula P= 4V2, where V is the velocity and P the pressure gradient. This simple yet powerful formula rapidly gained acceptance among clinicians and was later nicknamed the “E = mc2 equation of cardiology”.
An ultrasound imaging unit fit for industry
The next step was to establish clinical trials utilising the new technology. In summer 1976, Angelsen employed five engineering students at NTNU to assemble 10 Doppler ultrasound units. The team sold four of the devices, and the rest stayed in Trondheim.
In those early days, the instrument did not have an imaging unit and was difficult to use in the clinical setting. Despite these challenges, cardiologist Dr Liv Hatle used one to pioneer a new technique for diagnosing heart disease (read the article here), while Dr Sturla Eik-Nes used another to study foetal blood flow in pregnant women [5].
To improve clinical usability, the engineers made many upgrades, taking advantage of the analogue-to-digital electronics boom happening at the time. Angelsen emphasises that “the work we did came in parallel with this development of instrumentation, integrated circuits and computers.”
The researchers and clinicians in Angelsen’s group in Trondheim knew that they needed an industrial partner to develop an imaging unit for the PEDOF technology and to launch it commercially, so they turned to a small start-up based in Horten called Vingmed AS. Together they created the CFM 700, the world’s first annular array colour flow imaging system. The system was introduced to the world at the American College of Cardiology meeting held in Atlanta, Georgia in March 1986.
Inspiring a new generation of healthcare innovators
Four of Angelsen’s students who had worked on the first Doppler prototypes in summer 1976 later became important figures in Norwegian tech. Kjell Kristoffersen became Head of Technology at GE Ultrasound, Arne Grip founded Medistim, Sverre Horntvedt became CEO of Sensonor, and Veroslav Sedlak became Head of Business Development at Goodtech.
Undoubtedly, Angelsen’s invention has laid the technological and theoretical basis for a thriving Norwegian medical ultrasound industry. GE Healthcare has incorporated much of this innovation into its ultrasound equipment and services market, which now reports annual revenues of USD 3 billion .
Despite a monumental career in medical ultrasound spanning 50 years, Bjørn Angelsen has never rested on his laurels and shows no signs of slowing down. In 2010 he founded the start-up SURF Technology as a spin-off from his research group at NTNU and continues to inspire the next generation of healthcare innovators.
10 Common Applications of Artificial Intelligence in Health Care
Many industries have been disrupted by the influx of new technologies in the Information Age. Healthcare is no different.Particularly in the case of automation, machine learning, and artificial intelligence (AI), doctors, hospitals, insurance companies, and industries with ties to healthcare have all been impacted – in many cases in more positive, substantial ways than other industries.
According to a 2016 report from CB Insights, about 86% of healthcare provider organisations, life science companies, and technology vendors to healthcare are using artificial intelligence technology. By 2020, these organisations will spend an average of $54 million on artificial intelligence projects.
So what solutions are they most commonly implementing? Here are 10 common ways AI is changing healthcare now and will in the future.
1. Managing Medical Records and Other Data
Since the first step in health care is compiling and analyzing information (like medical records and other past history), data management is the most widely used application of artificial intelligence and digital automation. Robots collect, store, re-format, and trace data to provide faster, more consistent access.
2. Doing Repetitive Jobs
Analyzing tests, X-Rays, CT scans, data entry, and other mundane tasks can all be done faster and more accurately by robots. Cardiology and radiology are two disciplines where the amount of data to analyze can be overwhelming and time consuming. Cardiologists and radiologists in the future should only look at the most complicated cases where human supervision is useful.
3. Treatment Design
Artificial intelligence systems have been created to analyze data – notes and reports from a patient’s file, external research, and clinical expertise – to help select the correct, individually customized treatment path.
5. Virtual Nurses
The startup Sense.ly has developed Molly, a digital nurse to help people monitor patient’s condition and follow up with treatments, between doctor visits. The program uses machine learning to support patients, specialising in chronic illnesses.
In 2016, Boston Children’s Hospital developed an app for Amazon Alexa that gives basic health information and advice for parents of ill children. The app answers asked questions about medications and whether symptoms require a doctor visit.
6. Medication Management
The National Institutes of Health have created the AiCure app to monitor the use of medication by a patient. A smartphone’s webcam is partnered with AI to autonomously confirm that patients are taking their prescriptions and helps them manage their condition. Most common users could be people with serious medical conditions, patients who tend to go against doctor advice, and participants in clinical trials.
7. Drug Creation
Developing pharmaceuticals through clinical trials can take more than a decade and cost billions of dollars. Making this process faster and cheaper could change the world. Amidst the recent Ebola virus scare, a program powered by AI was used to scan existing medicines that could be redesigned to fight the disease.
The program found two medications that may reduce Ebola infectivity in one day, when analysis of this type generally takes months or years – a difference that could mean saving thousands of lives.
8. Precision Medicine
Genetics and genomics look for mutations and links to disease from the information in DNA. With the help of AI, body scans can spot cancer and vascular diseases early and predict the health issues people might face based on their genetics.
9. Health Monitoring
Wearable health trackers – like those from FitBit, Apple, Garmin and others – monitor heart rate and activity levels. They can send alerts to the user to get more exercise and can share this information with doctors (and AI systems) for additional data points on the needs and habits of patients.
10. Healthcare System Analysis
In the Netherlands, 97% of healthcare invoices are digital. A Dutch company uses AI to sift through the data to highlight mistakes in treatments, workflow inefficiencies, and helps area healthcare systems avoid unnecessary patient hospitalisations.
These are just a sample of the solutions AI is offering in the healthcare industry. As innovation pushes the capabilities of automation and digital workforces, from providers like Novatio, more solutions to save time, lower costs, and increased accuracy will be possible.
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THE FUTURE OF AI
Artificial Intelligence (AI) has become an important aspect of the future. This applies equally as well to Information Technology (IT) as it does many other industries that rely on it. Just a decade ago, AI technology seemed like something straight out of science fiction; today, we use it in everyday life without realizing it – from intelligence research to facial recognition and speech recognition to automation.
AI and Machine Learning (M.L.) have taken over the traditional computing methods, changing how many industries perform and conduct their day-to-day operations. From research and manufacturing to modernizing finance and healthcare streams, leading AI has changed everything in a relatively short amount of time.
AI and related technologies have had a positive impact on the way the IT sector works. To put it simply, artificial intelligence is a branch of computer science that looks to turning computers into intelligent machines that would, otherwise, not be possible without direct human intervention. By making use of computer-based training and advanced algorithms, AI and machine learning can be used to create systems capable of mimicking human behaviors, provide solutions to difficult and complicated problems, and further develop simulations, aiming to become human-level AI
According to the statistics, the AI market is expected to reach $190 billion by 2025. By 2021, global spending on cognitive and AI systems will reach $57.6 billion, while 75% of enterprise apps will use AI technologies. In terms of national GDPs, AI is expected to boost China by 26.1% and the United States by 14.5% by 2030.
On a more local level, some 83% of businesses say that AI represents a strategic priority, while 31% of creative, marketing, and IT professionals look to invest in AI technologies over the following 12 months. Similarly, some 61% of business professionals point to AI and machine learning as their most significant data initiative over the coming year. In addition, some 95% of business executives who are skilled in using big data also use AI technologies.
The Impact of AI in Information Technology
The digital transformation and adoption of AI technologies by industries has given rise to new advancements to solve and optimize many core challenges in the IT industry. Among all tech applications, AI sits at the core of development for almost every industry, with Information Technology being among the first. The integration of AI systems with W.T. has helped reduce the burden on developers by improving efficiency, enhancing productivity, and assuring quality. If the development and deployment of IT systems at large scale were next to impossible, through AI’s development of advanced algorithmic functions this is now possible.
More Secure Systems
Data security is of critical importance when it comes to securing personal, financial, or, otherwise, confidential data. Government and private organizations store large amounts of customer and strategic data that needs to be secure at all times. By using advanced algorithms and by making use of Machine Learning, Artificial Intelligence can provide a necessary level of protection to create a high-security layer within all of these systems. AI will help identify potential threats and data breaches, while also providing the needed solutions and provisions to avoid any existing system loopholes.
Enhanced Coding Productivity
Artificial Intelligence also uses a series of algorithms that can be applied directly to help programmers when it comes to detecting and overcoming software bugs, as well as when it comes to writing code. Some forms of Artificial Intelligence have been developed to provide suggestions when it comes to coding, which, in turn, helped increase efficiency, productivity, and provide a clean and bug-free code for developers. By looking at the structure of the code, the AI system will be able to provide useful suggestions, not only improving the overall productivity but also help cut on downtime during the production process.
Increased Automation
One major benefit of automation is that a lot of the “legwork” can be achieved with minimal or no human intervention. By using deep learning applications, IT departments can go a long way in automating backend processes that can enable various cost savings and minimize human hours spent on them. Numerous AI-enabled methods will also improve over time as their algorithms learn from their mistakes and improve their effectiveness.
Better Application Deployment During Software Development
When we talk about application deployment control, we need to take into account the various stages that go into software development. This means that the software versioning control is critical and highly beneficial during the development stage. And since AI is all about predicting possible issues, it has become an integral and highly-useful tool in detecting and anticipating problems during this stage. As such, these can be avoided and/or fixed without any major hiccups, meaning that developers will not have to wait until the final stage before improving the app’s overall performance.
Improved Quality Assurance
Quality assurance is, in large part, about ensuring that the right tools are used during the development cycle. To put it somewhat differently, AI methodologies can help software engineers use the right tools to fix various bugs and issues within the applications and adjust them automatically during the development cycle.
Better Server Optimization
Quite often, the hosting server will be bombarded by millions of requests on a daily basis. Whenever this happens, the server needs to open web pages that are being requested by users. Because of the constant flow of requests, some servers may become unresponsive and end up slowing down over the long term. AI can help optimize the host service so as to improve customer service and enhance the overall operations. As IT needs will progress, AI will be increasingly used to integrate those IT staffing demands and provide more seamless integration between the current business and technological functions.
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