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How Artificial Intelligence improves Healthcare 

The society around us is evolving. Planet Earth has now more devices than people, and everything has become more interconnected than we could imagine. People utilize AI systems, self-driving cars, Internet software to meet new friends and Google to look up every indication of illness.  

Each electronic activity generates a virtual byproduct that is used to learn about our style of living. Big data and analytics are the heart of the success of many of the world’s most popular businesses, from YouTube to Airbnb, Siri to Facebook. Despite being sensitive to the advantages of AI, Healthcare has been sluggish to accept the quickly growing technological tools, especially when compared to areas like banking, entertainment, and transportation: that is, until now.  

New prototype solutions with increasingly solid health and participation benefits are being developed by research universities and start-ups. Phones and computers have accelerated the mixing of technology and medicine, allowing for a multitude of discoveries that continues to enhance people’s lives. Individuals may now check their health without the help of a medical provider; therapy has now become accessible and no longer restricted to the doctor’s office. 

Information has showed to be an essential factor in enhancing the results of an illness. Machine learning, or the analysis of huge datasets, its interpretation, and sophisticated computer algorithms are now becoming a cornerstone for gaining ground and progress in the medical industry. Data is the golden ticket to democratizing and personalizing treatment through digital health. This coincides with an increasing need to quantify and assess more areas of the human life.  

Rapid technological innovation is facilitated by data insight and hard evidence. The ramifications of virtual care democratization are not just health-related, but also ethical. Organizations can use data mining and analytics to find hidden patterns and trends, as well as to make future forecasts. In this case, what would be the exact benefits? 

Although AI entities are uncertain to totally replace medical professionals in the near future, Automation and Machine Learning are revolutionizing the healthcare practice and improving the final results. Technology is aiming to increase diagnostics accuracy, forecast prognosis, and start the revolution of precision therapy.  

Diagnosis has become a lot easier 

Consider the following scenario:  A person goes to a specialist with discomfort in his thorax. The doctor inputs the signs and symptoms into a system after talking with the patient, which pulls up the most up-to-date clinical evidence than need to be reviewed in order to accurately diagnose the illness. A computer application supports the clinician in detecting any issues that would be too small for the radiologist’s eye to see on the Xray. While a daily sensor tracker collected a real-time record of his internal parameters fluctuations, the person’s phone may have been continually gathering basic information such as movement, sleep, heart rate, and temperature.

Finally, an app evaluates the patient’s medical data and family’s health history to offer treatments routes that are specifically tailored. Aside from data security and accountability, the possibilities of what we may learn from merging different quantities of information is fascinating. 

Various software and applications can be used as a substitute for non-emergency health care. In the coming years, merging the genetic information with neural network models will allow researchers to understand more about illness probability, enhance drugs interactions, and optimize patient treatment steps. 

Helping industry with Drug Development 

Artificial Intelligence has the ability to be used in advanced drug development for a multitude of purposes, ranging from first tests of bioactive agents to predicting the likelihood of success based on biological or physical parameters. Next-generation sequence analysis is an example of Research and Development technology. Deep learning can assist in structure-based drug discovery by predicting the 3D protein structure. It also offers the potential to simplify drug studies procedures by enabling organizers and research institutions to follow the evolution of the patient from the comfort and privacy of one’s own home, assess treatment responses, and maintain track of patient involvement. 

Prediction and prevention is the best treatment 

Currently, there are tools in operation that analyze information in order to forecast transmission of pathogens. This is frequently accomplished by combining real-time datasets such as social networking sites with historical data from search engines and other media. Artificial neural networks have been used to forecast malaria outbreaks by evaluating data such as precipitation, altitude, humidity, the incidence, and other variables. 

The next wave of personalized care  

Every patient comes with a massive database collection containing genetic sequences as well as patient registries such as health records and allergies. This has empowered professionals to examine specific individuals and their illnesses in ways that they previously couldn’t. Physicians may now use deep learning to identify patterns, correlations, and abnormalities in data, which can enable them to make better judgments. 

Because each person has a distinctive variant of the DNA sequence, doctors will have to be able to identify specific gene mutations or variances that may cause certain conditions in order to prevent them from occurring. This knowledge allows for more complete disease control programs to reduce hazards when they do occur. 

Building the medical instruments of the future 

MRI machines, CT scanners, and X-rays produce diagnostic pictures that provide non-invasive understanding of the functioning of the inside human organism. Some diagnostic procedures, however, primarily depend on actual tissue samples taken via biopsies, which come with hazards such as infections and procedural risks. 

Scientists believe that machine learning will empower the next wave of radiological instruments to be precise and comprehensive enough to eliminate the requirement for samples to be taken in some circumstances. 

This is where we come into play. 

XVision develops a software platform that is using Artificial Intelligence to help radiologists analyze medical images faster and more efficiently. Our AI-based algorithms are seamlessly integrated into the doctor’s workflow, helping them analyze chest x-rays and lung CTs up to 25% more efficiently. 

Our team is dedicated to creating the best digital radiology system for medical imaging interpretation. XVision offering includes pathologies detection, localization, and automatic measurements, all of them sent directly to the hospital PACS system.  

XVision In-house created algorithms cover all of a clinician’s most common requirements when it comes to chest image analysis and pulmonary nodules evaluation. Our solution is built around the radiologist’s everyday workflow to increase productivity and efficiency. The Machine Learning operations can enable doctors to identify critical work and focus on the most vital activities by allowing them to easily monitor workloads and processes. 

We make medical imaging technologies better, by providing disruptive solutions like bone suppression and subtraction, automatic calculation of the cardio-thorax index, COVID-19 lesion quantification on lung CTs, pathologies localization and detection, triage and prioritization, pulmonary nodules identification and measurements. All of them powered by AI.  

We are XVision

You can also read about how XVision obtained € 1 million in a new round of funding by clicking here.

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