UAE strategy for Artificial Intelligence in the health sector is focused on assistance with chronic and dangerous diseases. Improvement with these issues can be achieved in many ways, as AI in healthcare is now a quickly growing topic.
When visiting a doctor becomes cumbersome, because of the distance or inconvenience, telemedicine comes into play. A worried parent can call or video chat with a doctor and receive advice on that new rash – is it worth further checking out or can a drugstore cream fix it. Still, this process can be streamlined even more with AI. As the doctor’s time is very valuable, it would be ideal to bring them only on more serious or uncertain cases. With the use of CNNs trained on a database of rash images, AI model can provide initial diagnosis and even a level of certainty about the result. And when the issue is not quite as visible as rash, AI-powered smart apps, taught on expert knowledge can perform a patient interview tailored to their needs, and ask the right questions, so no important information slips by.
Assistance with disease detection
As the global society gets older and chronic diseases more prominent, access to medical staff, especially doctors, can become difficult. One of the solutions involves assisting the staff in medical diagnosis and disease detection with the help of AI solutions – most frequently ones based on Deep Learning.
When it comes to medical imaging analysis, Convolutional Neural Networks (CNNs) have the absolute lead. Networks can train directly on real-life images, either X-Rays, Computer Tomography (CT) or Magnetic Resonance Imaging (MRIs), coupled with a diagnosis of a medical professional. When the diagnosis is conveyed in the long text format, methods of Natural Language Processing (NLP) can be used. Otherwise, when images are simply tagged, CNNs can work directly. This kind of approach is already used to allow early diagnosis of breast and lung cancer, pneumonia or ocular diseases, among many more, by performing Object Detection and Object Recognition tasks. With the recent creation of efficient Recurrent Neural Networks (RNNs) sound and signal medical data also became easily interpretable for AIs. Current solutions can spot heart murmurs or chest sound irregularities along with detection of abnormalities in the echocardiogram.
New fields of use of AI in healthcare are constantly explored and evaluated in clinical trials, as novel techniques such as Deep Learning are robust and allow doctors to look less on pictures and more on patients.
Next sector where AI can have a great impact is in education. UAE strategy includes cutting costs and increasing the desire for education. Just as with medicine – the specialized person’s time is the most valuable and when it comes to teachers, there is always a need for more of their time, attention and presence. AI can help with some of the administrative educational tasks, but also as importantly, can allow for faster, more intuitive and in-depth learning by customizing the curriculum to each student’s needs, allowing for a much-needed break for overburdened teachers.
Automated assignments checking and plagiarism detection
While simple, closed-form tests are being checked in a computerized manner for years now, more open-form written or math assignments seemed to be off-limits. With the use of modern Deep Learning solutions, like CNNs, recognition, and digitalization of hand-writing is an easy task – even including complicated mathematical formulae. And from there, models powered by RNNs in pair with NLP techniques, can gain insight into written content and offer suggestions for both students and teachers alike. Moreover, having the work digitized and structured insightfully allows for easy plagiarism checking with greater accuracy and less risk for false-positives than currently widely used techniques that base only on text to text comparisons.
Tailored education paths
Using interactive, AI-powered apps for learning allows for tracking each student’s progress closely like never before. Machine Learning (ML) algorithms can detect and remember where the particular person struggles and create a feedback loop – offering new ways to relearn and solidify existing knowledge.Classroom courses are necessarily targeted for 30 students at the time and hence standardized to some arbitrary normal learning tempo. The AI approach allows fast learners to glance over the simplest parts and learn material in a more efficient way. The slower learners can have their well needed time and get new chunks of information only when they’re ready.
Educating for the future
Our rapidly changing world more and more requires not only fluency in a computerized world but also an artificially intelligent one. Machine Learning algorithms already allow for more efficient and accurate results in various fields and the future holds only more use of AI solutions. Young students need to be aware of the inner workings of systems surrounding them every day and there seems to be no better way to future-proof their education by using AI itself to provide the guidance.
Probably there isn’t a better way to summarize the importance of AI in education than to say, that this article was written with the skilled help of at least two different AI systems – a writing assistant and a smart spellchecker.
While topics of nature and AI may seem completely disconnected, actually the opposite is true. As caring for the environment requires not only performing specific actions but also a constant observation of the results, ML techniques can help with the automation of the most time-consuming tasks. UAE strategy focuses on increasing the forestation rate, so let’s see how AI can be helpful in this area.
Soil example assessment
ML algorithms can be trained to use Object Recognition and Regression methods to assess the quality of the soil. Such information can be used to decide what species of tree will have the best chance of thriving when planted and determine the best kind of follow-up care. Or, information can be used to increase the plantation efficacy and yield, thus leaving more space for forestation actions.
Illegal deforestation is a worldwide problem that leads to disruption of natural ecosystems and increases in number and severity of adverse weather events, like droughts or floods. Traditionally, monitoring for such activities has proven difficult, mostly because of the large area that needed to be covered. Thanks to Computer Vision algorithms, like CNNs or RNNs, illegal logging can be automatically recognized in the wide range of visual sources like satellite images (both visible and infrared wavelengths), images or videos from planes or drones. Even community-sourced smartphone pictures can be interpreted and incorporated into the recognition software.
Water levels monitoring
With the climate rapidly warming, constant monitoring of the water levels became a necessity. As in the case of illegal deforestation, Object Recognition methods can be utilized to create a system that constantly monitors for water level changes and performs Anomaly Detection – meaning it only rises alarm if the level fluctuation is beyond the norm or level change pattern is somehow worrying.
The use of AI for automatic monitoring and assessment allows for resource-saving and their redirection towards new and efficient actions, which will, in turn, lead to a much-needed increase in forestation rate.
At LogicAI, we have extensive experience and a deep understanding of novel Machine Learning techniques, like Deep Learning, CNNs, RNNs, and NLP and will be happy to help with solutions in any of the aforementioned fields. So if you’re interested, please don’t hesitate to contact me at email@example.com.
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