CoronaVirus HealthTech

AI Is Screening Billions of Molecules for Coronavirus Treatments

One of the best features of AI, especially when used for science, is that it’s fast and efficient. Due to the global coronavirus spread, these days, AI has been utilized in medicine often. Recently, AI is screening billions of molecules for coronavirus treatments.

AI Is Screening Billions of Molecules for Coronavirus Treatments

A staggering speed

How much time would take evaluating 1 billion small molecules for their ability to bind SARS-CoV-2 proteins on casual computers? On the largest of them, probably around a decade. AI has shown us an amazingly variated speed in that matter. The work moved quickly. In a pandemic, “there are many drug candidates people would like to screen and, even with the proliferation of cloud and supercomputers, there just wouldn’t be enough computing to test them all” – says Shantanu Jha, a computational scientist at Rutgers University and Brookhaven National Laboratory. Together with his colleagues, they have integrated machine learning into the simulations they run on the supercomputers. It allows the programs to adapt to new information that gets uncovered as they run, thereby producing lists of candidate small molecules, much faster than traditional supercomputing methods.

Pilot study

COVID-19 cases continue to rise. The common ground that physicians from all over the globe seem to follow is repurposing existing drugs. The goal is clear – we need to find a cure that will help patients immediately get better. In a pilot study at the end of March, twelve COVID-19-infected people were admitted to the hospital in Italy. They received a daily dose of the rheumatoid arthritis drug, baricitinib, and an anti-HIV drug combination of lopinavir and ritonavir. Another study group of 12 received only lopinavir and ritonavir. Both groups were using it for two weeks. After the treatment, the patients who received baricitinib had mostly recovered – no coughs, no fevers, no short breath. Seven of them had been discharged from the hospital. The group who didn’t get baracitinib was still exhibiting symptoms. An artificial intelligence (AI) company based in the United Kingdom, BenevolentAI, was crucial to conduct this study. 

The team completed the work in only a few days

Justin Stebbing, an oncologist at Imperial College London and BenevolentAI collaborator, claims AI “makes higher-order correlations that a human wouldn’t be capable of making, even with all the time in the world. It links datasets that a human wouldn’t be able to link”. BenevolentAI’s vice president of pharmacology, Peter Richardson, says that it took him only an afternoon of work to use the company’s knowledge graph – an enormous, digital storehouse of biomedical information and connections inferred and enhanced by ML – to identify two human protein targets to focus on, AP2-associated protein kinase 1 (AAK1) and cyclin g-associated kinase (GAK).

These kinases mediate endocytosis – a process by which cells engulf things, including viruses – and, if disrupted, might make it harder for SARS-CoV-2, the virus that causes COVID-19, to get into human cells. Having addressed those targets, they used another algorithm to find existing drugs that could hit the protein targets. The whole process took a few days – a situation impossible to imagine just a few decades ago when AI hasn’t been born yet. 

Next steps to succeed with AI-derived treatment

Eventually, the researchers cut the list of drugs for coronavirus down to about 30. Among the handful of those that showed the highest affinity for binding their targets (like toxic chemotherapy drugs, and others) baricitinib won clearly. The side effects were mostly benign and showed up after a longer period of treatment than COVID-19 patients need. It’s not metabolized by the liver and is instead excreted through the kidneys. It means that it might be safe to combine it with a traditional antiviral (such as lopinavir) that is metabolized by the liver. In addition to baricitinib’s predicted interactions with AAK1 and GAK, it’s a known Janus kinase (JAK) inhibitor. Because JAK mediates cytokine signaling that leads to inflammation, inhibiting JAK suppresses inflammation, which, at first blush, might have been a problem. Baricitinib not only prevents the virus from getting into cells but also reduces the intense immune reaction that causes so many problems, even as viral titers start to fall.

The pharmaceutical company takes up the AI idea 

Eli Lilly, the pharmaceutical company that makes baricitinib, has agreed with the National Institute of Allergy and Infectious Diseases to study the drug’s effectiveness in COVID-19 patients in the US. The BenevolentAI team is one of several groups leveraging AI to find drugs that have already been approved by regulators and could, therefore, be repurposed to fight SARS-CoV-2.

What to believe in?

Coupling artificial intelligence techniques and algorithms with high-performance computing simulations to speed up the ability to screen billions of existing drugs for their interactions with and ability to disrupt SARS-CoV-2 proteins – this is what scientists do these days. Thanks to great minds and AI systems, the effectiveness of drugs against coronavirus will save a great number of lives.

Alzheimer's Disease HealthTech

Predicting The Risk of Alzheimer’s Disease With AI

These days, armed with medical and scientific knowledge and with the newest digital tools, we are going to great lengths to fight every illness. No wonder that new Artificial Intelligence (AI) medical algorithms arise often. Alzheimer’s disease is the primary cause of dementia worldwide, with one in 10 people aged 65 and older exhibiting Alzheimer’s dementia. The newest AI systems can accurately predict and diagnose the risk of it. Let’s browse the scientists’ brand-new Alzheimer’s disease with AI, interesting discoveries. 

Predicting The Risk of Alzheimer’s Disease With AI

Current knowledge on treating Alzheimer’s disease

Pharmaceutical companies have invested hundreds of billions in Alzheimer’s research, however, the field has had little success over the years, with 146 unsuccessful attempts to develop drugs that prevent or treat the disease, between 1998 and 2017. Only four new medicines received approval and solely to treat symptoms. More than 90 drug candidates are still in development. Now, the Massachusetts Institute of Technology (MIT) researchers are looking to partner with pharmaceutical firms to get the model implemented in real-world clinical trials for Alzheimer’s. 

Alzheimer’s disease and AI

With the use of Artificial Intelligence, researchers from MIT took time to develop tools to determine whether patients at high risk of Alzheimer’s disease would experience cognitive decline. The algorithm was able to predict patient cognition test scores for up to two years in the future. The deep learning algorithm, developed by researchers at the Boston University School of Medicine, uses a combination of brain magnetic resonance imaging (MRI) testing to measure cognitive impairment, along with data on age and gender, which helps to accurately predict the risk of Alzheimer’s Disease. For new participants, researchers developed a second model that is personalized for each patient and continuously updates risk scores. 

Meta-learning scheme

To optimize the results of the personalized models, and to use newly recorded data at its best, researchers invented a “meta-learning” scheme. It learns to automatically choose which type of model works best, for any given participant, at any given time. While meta-learning has been used before for computer vision and machine translation tasks, the team said this is the first time when it’s used for Alzheimer’s. The meta-learning scheme helped to reduce the error rate for future predictions by fifty percent. “We wanted to learn how to learn with this meta-learning scheme. It’s like a model on top of a model that acts as a selector, trained using meta-knowledge to decide which model is better to deploy” – says Oggi Rudovic, a Media Lab researcher.

MRI scans at use. Expanding the use of neuroimaging data

How to create a useful database for treating Alzheimer’s medical predictions? The researchers collected MRI scans of the brain, demographics, and clinical information of individuals with Alzheimer’s disease as well as ones with normal cognition. Afterward, they developed a novel deep learning model to accurately predict Alzheimer’s disease status on the other independent cohorts.

This study has broad implications for expanding the use of neuroimaging data (among which MRI scans) to accurately detect risks. Researchers first trained a population model on an entire dataset that included clinically significant cognitive test scores and other biometric data from Alzheimer’s patients and healthy individuals between biannual doctor’s visits. The model analyzes data learning patterns that can help predict how patients will score on cognitive tests.

Preparing for the future

Why do we need these models? They could help with selecting candidate drugs and participants for clinical trials, and allow patients and their loved ones prepare for rapid cognitive decline. “Being able to accurately predict future cognitive changes can reduce the number of visits the participant has to make, which can be expensive and time-consuming. Apart from helping develop a useful drug, the goal is to help reduce the costs of clinical trials to make them more affordable and done on larger scales” – said Oggi Rudovic, a Media Lab researcher.

This algorithm can generate interpretable and intuitive visualizations of individual Alzheimer’s disease risk. These steps could be extended to other organs in the body, leading to improved drug development and care delivery. We can also already assume that it’s possible to diagnose other degenerative diseases with the help of AI. “If we have accurate tools to predict the risk of Alzheimer’s disease […], that is readily available and which can use routinely available data, such as a brain MRI scan, then they have the potential to assist clinical practice […]” – says Vijaya Kolachalama, Ph.D., assistant professor of medicine at Boston University School of Medicine.

Artificial Inteligence HealthTech

What Makes Food Delivery Industry Bet Big on Artificial Intelligence and Machine Learning

If you aren’t directly involved in the food-delivery industry (on the other hand, who isn’t, at least passively?), you don’t really think about how many cutting-edge technologies are being used for its upgraded functioning. It’s been years since we had to make exhausting calls at the pizza place, listing our favorite ingredients to the person on the other side of the phone. In the blink of an eye, things became so much easier and more comfortable. Not surprisingly, it’s quite a task to gain a front-liner advantage in this industry nowadays. 

What Makes Food Delivery Industry Bet Big on Artificial Intelligence and Machine Learning

Get in touch with experts 

Leading fast-food chains and food delivery companies are working on creating apps optimized by artificial intelligence and advanced machine learning algorithms to display the most relevant messages and offers to their users. If you are a professional operating in the business somehow related to food-delivery, you should talk to your analytics experts about the newest possibilities that AI and ML bring to the table. Specialists will provide you comprehensive insights on how artificial intelligence and machine learning for the food and beverage delivery industry are transforming the whole industry at its very core. Quantzig, global data analytics and advisory firm that delivers actionable analytics solutions, to resolve complex business problems, presents some comprehensive insights. The company has offices in the US, UK, Canada, China, and India, assisting clients across the globe with end-to-end data modeling capabilities to leverage analytics for prudent decision making.

The use of updated knowledge on AI and ML

Whether you are in it for a long haul or you’re not initially interested in this enigmatic topic, you should know a bit more. Get acknowledged with how you can use cases of machine learning for the food delivery industry, gain insights into factors driving innovation, and enhance performance by leveraging machine learning. Here are some reasons why machine learning for the food delivery industry is important. Such technologies deliver fact-based results to online food delivery companies that possess the data and the required analytics expertise. According to experts, “Food delivery industry players are now revolutionizing the food industry by leveraging artificial intelligence and machine learning to enhance their market reach and customer satisfaction rates”. After several years of being confined to technology labs and the pages of sci-fi books, today AI and big data have become the dominant focal point for businesses across industries.

Enhancing market reach and customer satisfaction rates

The food delivery industry is immensely popular among millennials. It’s not hard to guess why. It’s convenient, and its usage is easy and comfortable. Along with that, there comes the increasing competition among food delivery industry players to improve customer retention rates and improve product quality. Exploring new ways of development in this direction became a must for companies. Big data, artificial intelligence, and machine learning came into the picture. What’s more, in the future, people don’t just want more technology in products and services. They want more human technology. 

Reasons why machine learning for food delivery industry is so important

AI and ML play an integral role in predicting food trends, helping online food delivery companies to identify and capitalize on the new flavors that are most preferred by their customers. They improve operational efficiency. ML for the food-delivery industry helps to understand customer behavior better and provide customized services. AI and ML analyze factors like the impact of temperature on food or the impact of market trends on consumption. The newest technologies enhance customer relationships. The proliferation of AI and machine learning in the food industry have contributed significantly to the growing popularity of chatbots. It is growing immensely popular due to its ability to drive better customer experiences. The new technology can drive online experiences and enhance market share by targeting tech-savvy users. 

Another reason for choosing brand-new technology for your service? Time matters.

AI and LM provide more than accurate delivery time estimates.  Machine learning for the food delivery industry helps to collect real-time data about traffic and route plans. Hence, it provides companies with an accurate estimation of the delivery time. What comes with it? The food delivery industry companies can take preventive measures for food wastage. Combined, AI and ML can predict the impact of factors, like being late with a delivery, on food items.   

Why opt for AI and ML? 

As cognitive technologies transform the way people use online services to order food, it becomes imperative for online food delivery companies to comprehend customer needs, identify the dents, and bridge gaps, by offering what has been missing in this business. The combination of big data, AI, and machine learning is driving real innovation, so don’t think twice before leveraging them.

Artificial Inteligence HealthTech Technology

Drones Vs. Covid-19: Ensuring Airspace Safety And Security With AI

With the spread of COVID-19, few essential changes have been introduced to social behaviors. Whilst we often consider them as exaggerated, they are required to lower the rate of transmission and avoid overwhelming national healthcare systems. The Covid-19 pandemic has demonstrated not only that drastic measures are essential, but also that we urgently need to use the newest technology. Let’s take a look at Drones Vs. Covid-19

The most innovative measures across the globe

Across the globe, cutting-edge technology was used to efficiently knack Covid-19 over. Means include using 3D printers to produce face shields and ventilators, deploying hospital robots to transport equipment and protect doctors and nurses from the virus. Not less important are drones. For example, In India, the government is using drones to sanitize public spaces in areas with confirmed cases, such as hospitals and metro areas. They use 300 drones across the state to spray a disinfectant solution approved by the WHO. On the other hand, in France, Italy, and Spain, authorities use drones to monitor social distancing and enforce coronavirus restrictions with loudspeakers. With nearly 70,000 tests conducted so far, Ghana has one of the highest testing rates in Africa. How do they distribute them? Easy to guess, with drones! Drone delivery, sanitization, and enforcement measures enable a quicker response to the pandemic and help save lives.

Drones’ use in America

Where some of the similar actions taken by the government of the United States? These widespread protective measures just wait to be successfully copied. As for now, the challenge many businesses, nonprofits, and local authorities face is regulatory approval. The Federal Aviation Administration’s Part 107 rules for unmanned aerial systems include flying below 400 feet, in altitude within visual line of sight, during daylight hours. The FAA enables near real-time airspace authorization for flights that comply with these rules, but a Part 107 waiver is required to operate drones at night, over people, or beyond visual line of sight. Problems arise as these advanced operations are often core to missions that can help fight the coronavirus pandemic.

Official requirements

Nearly 4,000 waivers were granted for advanced drone operations since the Part 107 rules’ implementation in 2016. At the same time, less than 200 were permitted for flights over people or beyond visual line of sight. Naturally, there is a reason for that. Regulators need assurances regarding the safety and security of the airspace. Commercial drone operators must prove that they are equipped to minimize risks and manage unforeseen circumstances. It couldn’t be underlined more how complicated it is to assure officials that we are not going to cause troubles up there.

Issues with low altitude airspace

At low altitude airspace, we aren’t provided with the air traffic services. Operators must be well-equipped, in order to plan, execute, and adapt their flights, as changes occur in the airspace. They are obliged to monitor regulatory dynamics (like temporary flight restrictions) as well as local conditions (like weather shifts), ground risks, and temporary objects (like construction cranes). Moreover, they need to monitor airspace traffic to maintain separation from other aircraft, including drones, helicopters, and planes. Not to mention, they need to protect their drones from a hardware malfunction and cyber threats that could put public safety in danger.

This long list of responsibilities understandably feels daunting. Fortunately, this is where AI enters to help. Being the key to analyzing crucial data (such as weather forecasts), ground risks, and vehicle performance, selecting the right drones for each mission, generating the safest flight paths, and autonomously adapting flights as conditions change – AI perfectly plays its role. Moreover, by being able to recognize hazardous objects, it’s critical to detecting and avoiding obstacles that otherwise would pose a danger to unmanned flights. 

The USA is known for its technological innovation and entrepreneurship

An AI-based approach, in aircraft safety, enables to analyze sensor data, across a drone fleet, and predict maintenance needs, before a failure occurs. From a security standpoint, AI can protect drones from previously unseen zero-day attacks that traditional anti-malware wouldn’t detect. Artificial intelligence can help fill the gaps in today’s airspace systems. It minimizes the responsibility put on drone operators, and it provides the safety and security assurances that regulators need when considering waivers for Covid-19 drone missions. It’s important to consider every option that can help fight the global crisis.

Artificial Inteligence

Artificial Intelligence Examines ECGs Results. It Predicts Irregular Heartbeat and Death Risk.

With all the AI innovations, it doesn’t surprise that sooner or later, we would have arrived at significantly sharpening medical science’s tools. The newest discovery which upgrades count? It looks like scientists have trained a computer (a neural network or artificial intelligence) to evaluate electrocardiograms (ECGs), to predict which patients are likely to develop an irregular heartbeat. What’s more, its predictions are effective even if human forecasts were different. The AI tool was finding predictions of heartbeat errors among those previously interpreted by doctors as normal. Researchers also created a neural network to examine ECGs, which identifies patients at increased risk of dying of any cause within the next year.

Artificial intelligence examines electrocardiogram (ECG) test results

This common medical test (ECG) serves doctors to make assumptions about the patients’ heart’s health. The new medical AI tool’s objective is to pinpoint patients at higher risk of developing a potentially dangerous irregular heartbeat, also known as arrhythmia, or of dying within the next year. The American Heart Association’s Scientific Sessions is an annual, premier global exchange of the latest advances in cardiovascular science. Two preliminary studies on the above-mentioned newest AI discoveries were presented there, on November 16-18, 2019 in Philadelphia. 

Electrocardiogram (ECG or EKG) – What is it and how does it work?

An electrocardiogram (EKG or ECG) is a test that measures the electrical activity of the heartbeat. Each beat is an electrical impulse (‘wave’) that travels through the heart. This wave causes the muscle to squeeze and pump blood from the heart. A normal heartbeat on ECG will show the timing of the top and lower chambers. An ECG gives two major kinds of information.

By measuring time intervals on the ECG, a doctor can determine how long the electrical wave takes to pass through the heart, in other words, if the electrical activity is normal or slow, fast or irregular. Second, by measuring the amount of electrical activity passing through the heart muscle, a cardiologist may be able to find out if parts of the heart are too large or are overworked. The EKG testing doesn’t hurt, and there’s no risk associated with having it done. The machine records the ECG, but it doesn’t send electricity into the body.

The phase of gathering database

To conduct the research, scientists used more than 2 million ECG results from more than three decades of archived medical records in Pennsylvania/New Jersey’s Geisinger Health System. Subsequently, they trained advanced, multi-layered computational structures – deep neural networks. These studies are among the first to use AI to predict future events from an ECG, rather than to detect current ones. Brandon Fornwalt, M.D., Ph.D., associate professor, and chair of the Department of Imaging Science and Innovation at Geisinger in Danville, Pennsylvania: ‘This is exciting, and provides more evidence that we are on the verge of a revolution in medicine, where computers will be working alongside physicians to improve patient care’. Looks promising, indeed!

A deep neural network for predicting incident atrial fibrillation directly from 12-lead electrocardiogram traces

Can a deep learning model predict irregular heart rhythms, known as atrial fibrillation (AF), before it develops? – scientists speculated. As atrial fibrillation is associated with a higher risk of stroke and heart attack, they decided to use AI to dig deeper into this matter, creating room for innovation and effective forecasts. The number of analyzed samples was spectacular – 1.1 million ECGs that didn’t indicate the presence of AF in more than 237,000 patients.

With the use of highly specialized computational hardware, scientists trained a deep neural network to analyze 15 segments of data — 30,000 data points — for each ECG. As predicted by the neural network, within the top 1% of high-risk patients, 1 out of every 3 of them was diagnosed with AF within a year. These model predictions demonstrated a longer-term prognostic significance that we haven’t been conscious of before. Patients predicted to develop AF at 1-year had a 45% higher hazard rate in developing AF over a 25-year follow-up than the other patients. As senior author Christopher Haggerty, Ph.D., assistant professor in the Department of Imaging Science and Innovation at Geisinger, said ‘many times, the first sign of AF is a stroke’. If this is the case, this cutting-edge AI tool seems to significantly change life’s opportunities.

Deep neural networks can predict one-year mortality directly from ECG signal even when clinically interpreted as normal

The results of 1.77 million ECGs and other records from almost 400,000 patients were analyzed to identify patients most likely to die of any cause within a year. Scientists compared machine learning-based models that either directly analyzed the raw ECG signals or relied on aggregated human-derived measures and commonly diagnosed disease patterns. What surprises is that the neural network was able to accurately predict the risk of death even in patients deemed by cardiologists to have a normal ECG. Sadly, these physicians were generally unable to recognize the risk patterns that the neural network detected. Fornwalt, who co-directs Geisinger’s Cardiac Imaging Technology Lab with Haggerty comments: ‘This could completely alter the way we interpret ECGs in the future’.

Artificial Inteligence

Keys for Healthcare Innovation? AI, Machine Learning, and Blockchain!

There is namely one positive effect of the COVID-19 pandemic. It shows us, on a global scale, how important it is to be open to innovation in healthcare. Coronavirus is acting as a catalyst for the newest research and experimentations with technology in this field. It appears that AI, machine learning, and blockchain are vital to facilitating this change. Blockchain technology is one of the most disruptive technologies in the world. This rapidly evolving field provides fertile ground for proof-of-concept testing, experimentation, and investment.​ Let’s acquaint ourselves with the blockchain’s features that are going to change the future of the healthcare industry.

The importance of taking efficient action against COVID-19

What does blockchain technology in healthcare offer? A special edition of OMICS: A Journal of Integrative Biology, has highlighted the importance of key digital technologies for innovation in healthcare in response to the challenges posed by COVID-19. Multiple industries are adopting blockchain technology to innovate the way they function – the healthcare industry is one of them. Blockchain technology has the potential to transform health care, placing the patient at the center of the healthcare ecosystem. Blockchain has all that is necessary to increase the security, privacy, and interoperability of health data. Vural Özdemir, MD, Ph.D., Editor-in-Chief of OMICS: “COVID-19 is undoubtedly among the ecological determinants of planetary health. Digital health is a veritable opportunity for integrative biology and systems medicine to broaden its scope from human biology to ecological determinants of health. This is very important.” 

Healthcare industry and its development

It will be impossible for us to overstate the importance of the healthcare industry. What’s even weirder, it’s one of the slowest growing industries in the entire space. Compared to two decades ago, hospitals still function pretty much the same way, because of the lack of innovation. Considering that this branch of the industry has the smartest and best-educated people in the world, the idea of no significant progress makes us wonder why it is so?

Challenges that come with the use of blockchain technology

As we read in the article by Erik Fisher, Arizona State University, Tempe, ‘Blockchain for Digital Health: Prospects and Challenges’: ‘[…] By reshaping both the technological and social environment, the rise of blockchain in digital health can help reduce the disparity between the enormous technical progress and investments versus our currently inadequate understanding of the social dimensions of emerging technologies through commensurate investments in the latter knowledge domain’. Forecasts say that blockchain technology in the healthcare market will cross $1636.7m (€1513.46m) by the year 2025.

A major concern? Privacy.

A lot of storing and sharing health data is needed to make use of the newest technology. With current healthcare data storage systems, lacking top-end security, it becomes hard to have everything under control. Yet, there is a quick fix to it. Blockchain can provide a solution to vulnerabilities such as hacking and data theft. But that’s not all. A variety of benefits such as time reduction, effective communication system, enhanced operational efficiency, can be achieved thanks to it. The use of blockchain means interoperability, and enabling the exchange of medical data among the different systems securely. The blockchain technology could provide a new model for health information exchanges (HIE) by making electronic medical records more efficient, disintermediated, and secure.

Vertical and horizontal progress

However, stating that no innovations have been done in the medical field, isn’t correct either. In the end, the average life expectancy has significantly increased. What does the “lack of innovation” really mean? This industry is rife with vertical innovation. However, it always lags when it comes to the horizontal one. This diversification is crucial to understand its future roads of development. 

Which problems can be eliminated with the use of blockchain?

One of the burning problems nowadays is how to efficiently counter fake drug supplies. The use of blockchain technology for claims adjudication and billing management application will register 66.5% growth by 2025, owing to errors, duplications, and incorrect billing. In 2017, in the US, nearly 400 individuals including doctors have been convicted for $1.3bn (€1.2m) fraud. The need to mitigate these problems in the future will, most probably, encourage specialists to adopt new technologies in this application segment.

Artificial Inteligence

Artificial Intelligence, Probabilistic Modeling Is Here to Improve the Survival of Cancer Patients.

Nothing is more important than health. This popular saying (might be even called cliche) is especially current nowadays, when a lot of people die, with no cure and no vaccines, because of the COVID-19 pandemic. Others leave this world equally unnecessarily. For example, those who weren’t diagnosed in time, and lose their fight with cancer. Currently, thanks to Artificial intelligence and its probabilistic modeling, we can significantly improve the prediction of survival in cancer patients. Globally, cancer is the second leading cause of death.

Artificial Intelligence, Probabilistic Modeling Is Here to Improve the Survival of Cancer Patients.

What can science do for cancer patients?

Apparently, it’s able to do a lot! Improving the prediction of survival indicators in patients with breast cancer is a real breakthrough. Not surprisingly, scientists are using tools from artificial intelligence and probabilistic modeling. One of the tools is ModGraProDep, an innovative system presented in a study led by Ramon Clèries, a lecturer at the Department of Clinical Sciences of the Faculty of Medicine and Health Sciences of the University of Barcelona, and member of the Oncology Master Plan/ICO-IDIBELL.

How do cancer cells behave?

When cancer emerges in the human body, it produces aggressive cells. They can evade the body’s growth control mechanisms. These cells are also invasive because they enter and subsume adjacent tissues. And they are often metastatic, which means that they travel and colonize distant sites in the body. One of the greatest unsolved challenges in cancer treatment concerns the frequent relapse of patients being treated by chemotherapy and the emergence of chemotherapeutic resistance in cancers. Mathematical models allow us to quickly search and identify the most effective drug combinations for cancer patients. They deepen our understanding of how and why cancer cells sometimes become resistant to chemotherapy drugs.

The study published in the journal Artificial Intelligence in Medicine

To sum up the cutting-edge technology in medicine regarding cancer, let’s first look at the team that took part in creating the newest study. The new technology’s research has been carried out by a team of experts on epidemiology, oncology and data management of the Oncology Master Plan – IDIBELL, the University of Barcelona, the Technical University of Catalonia, the Catalan Institute of Oncology (ICO), the Girona Biomedical Research Institute (IDIBGI), the University of Girona, the University of Alicante, the Epidemiology and Public Health Networking Biomedical Research Centre (CIBERESP), Carlos III Health Institute, the University Hospital Sant Joan de Reus, the Medical Oncology Service of ICO Girona, the Cancer Registry of Girona and Tarragona and the entity MC Mutual.

Numeric modeling – inputs, and outcomes

To cross another frontier in the fight against cancer, it’s worth reaching for mathematical modeling. There is no denying that mathematics plays an increasingly prominent role in cancer research. Mathematical Oncology—defined as the use of mathematics in cancer research—complements and overlaps with several other fields that rely on mathematics as a core methodology. One of the applications of numerical modeling for clinical indicators on oncology is the creation of predictive models to help oncologists and doctors to classify and value future scenarios of evolution in patients with cancer. Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. The prediction of survival in patients, considering variables and ages, is a decisive element to consider treatments and identify subgroups among the patients. For example, we already know that nearly half of Canadians will develop cancer in their lifetime, according to the Canadian Cancer Society. 

The application ModGraProDep Technology

Although the information is sometimes estimated through numeric modeling, often, there is not enough sample population to calculate these indicators specifically. The application ModGraProDep Technology (Modeling Graphical Probabilistic Dependencies) led to two studies. The scientific team that created it, designed a web application of great clinic interest in the field of oncology. It enables having a prediction of indicators on survival, and risk of cancer mortality of each patient for twenty years! In the first case, ModGraProDep enables users to identify the structure of the database, and to create a synthetic population of patients with the demographic features of the original cohort. They can, therefore, identify potential patterns of patients and calculate indicators. For example, the survival of a patient depending on the values of his or her variables. In a second study, it’s a new technology that can allocate values in a probabilistic manner in variables, for which there was no information gathered yet.

Artificial Inteligence

How AI Technology Is Changing Healthcare In Front Of Our Eyes!

It was never so visible to everyone. The current pandemic has underlined one shockingly fast development – the one, where AI technology and the health industry are joined and collaborate as never before. The future of healthcare, with advances in digital healthcare technologies such as 3D-printing, artificial intelligence, VR/AR, robotics, or nanotechnology, is currently right in the spotlight. Let’s explore how medical technology is efficiently reshaping healthcare.

How AI Technology Is Changing Healthcare In Front Of Our Eyes!

The closest future

The latest developments are at our hands, but, to get to know them, we need to get familiarized with some scientific explanations. We must control technology, and not the other way around! AI technology and healthcare workers need to cooperate and embrace emerging healthcare technologies in upcoming years. 

Will robots take over the jobs of nurses and doctors?

If you are scared that artificial intelligence will control the world within a couple of years or if you have nightmares about virtual reality, where we all live like zombies, then stop. Artificial Intelligence in the field of science will only serve us for the best! 

Dystopias, half-truths, and fake news

The fear about the unknown future, and about what it might bring upon us, is a real thing. Yet, no matter how scary it seems at the moment, we should pursue AI technological development in the field of science, especially medicine, because it’s saving lives. There is no doubt that our lives will be transformed soon, through various AI technologies. However, fear shouldn’t push us away from developing. We should overcome thoughts that make us anxious about the future. An open mind and an optimistic vision about the change is what we should be armed with.  

Which road to take? 

Technology can aid and improve our lives. The cooperation between people and technology will result in amazing achievements. Assuming that the goal can be summarized as healthier individuals living in healthier communities, digital technology could help transform unsustainable healthcare systems into sustainable ones. It can also equalize the relationship between medical professionals and patients, as well as provide cheaper, faster, and more effective solutions for diseases. Together, we can win the battle against cancer, AIDS, Ebola, or… the novel coronavirus!

A few words about Artificial Intelligence

It has been proven that Artificial Intelligence (aha AI) has the potential to redesign healthcare completely. Mining medical records, designing treatment plans, creating drugs faster? No medical professional can achieve comparable perfection in all these fields. In 2015, the start-up Atomwise launched a virtual search for safe, existing medicines that could be redesigned to treat the Ebola virus. As a result, they found two drugs that may significantly reduce Ebola infectivity. The same with breast cancer analysis. Google’s DeepMind algorithm outperformed all human radiologists on pre-selected data sets to identify breast cancer on average by 11.5%. These are only two of multiple examples, so imagine what the future will bring! 

When it comes to virtual reality…

VR is being used, for example, to train future surgeons. It’s also utilized by actual surgeons to practice operations. In the future, we will watch operations as if we wielded the scalpel. Software programs of a kind are developed and provided by companies like Osso VR and ImmersiveTouch. VR-trained surgeons had a 230% boost in their overall performance! They were faster and more accurate in performing surgical procedures. The AI technology is also benefiting patients when it comes to pain management. Women equipped with VR headsets who visualize soothing landscapes are calmer when they go through labor. Also, cardiac, gastrointestinal, neurological, and post-surgical pain have shown a decline in their pain levels when using VR for distraction.

What about augmented reality?

When it comes to augmented reality, users don’t lose touch with reality. AR technology puts information into eyesight as fast as possible. These distinctive features enable AR to become a driving force in the future of medicine. How? It might help, for example, medical students prepare better for real-life operations. It also enables surgeons to enhance their capabilities. At Case Western Reserve University, students are using the Microsoft HoloLens to study anatomy via the HoloAnatomy app, having access to virtual, detailed, and accurate depictions of the human anatomy. 

Artificial Inteligence

How Cutting-edge AI is Helping Scientists Tackle COVID-19

COVID-19 disease takes its toll throughout the world. No wonder that scientists are racing to find an efficient treatment or vaccine. It also shouldn’t surprise us that so widely-spread nowadays Artificial Intelligence could help them reach their goal.

How Cutting-edge AI is Helping Scientists Tackle COVID-19

Researchers needed

Our times are the times of research. Since we have so many tools and a lot of knowledge to discover and create everything, we cannot deal with the fact that there’s still a mystery that we are not able to reveal. This conundrum is called coronavirus pandemic. Researches on it have exploded in response to the COVID-19 pandemic, and now there are more than 60,000 papers online.

The meaning of AI

One of the most crucial tools of contemporary is Artificial Intelligence (AI) technologies like, for example, Natural Language Processing (NLP). It can help researchers tackle COVID-19 by processing huge amounts of data that otherwise wouldn’t be possible to deal with by humble human-beings. AI has the power to transform medical research in a way that we would never think about before. It helps us address extremely urgent medical challenges, such as COVID-19.

Machines can find, evaluate, and summarise innumerable research papers on the new coronavirus. A group of scientists would have to sacrifice all their lives to do it, and they still wouldn’t be sure whether they didn’t skip anything. Machines do it in seconds and couldn’t be more fierce about their results. Mind that thousands of new researches and other scientific documents are added every week as specialists race to find a vaccine or treatment. AI can also help us track the spread of COVID-19, as well as other diseases. How? By detecting early warning signals such as clusters of symptoms in a new place.

Is it really helpful? 

Specialists in Artificial Intelligence (AI) can use their training in concepts like statistical significance to understand medical researches regarding a sophisticated global database and extract from it the most relevant information. Machines can fruitfully assist us in reaching the ultimate goal. They can fastly detect patterns that we, as humans, may be slow to see. In other words, they can help us quickly understand data on the spread of COVID-19 and other diseases, and intervene fast and early.

Urgent questions need answers now, not later

Since there are about 67,000 scholarly papers related to the new coronavirus, and their number is still growing, every scientist working on a cure or vaccine must understand this prior research. In the worst case, hundreds of scientists would work on the same matter! To avoid errors and duplications, AI results in pretty helpful. Researchers and institutions around the world have rightly decided to tap the power of machines to overcome the COVID-19 crisis.

More about machine learning

Going through all these papers would take years. Machines could change this, providing us with an instant overview. In March, a partnership of several institutions, including the White House, created the COVID-19 Open Research Dataset (CORD-19). It’s a free resource for the global AI community. The database is just a beginning, though.

A brilliant one but still a beginning. AI specialists around the world work to develop tools to extract the most significant information using machine learning. This way, medical researchers will be able to keep up with this fast-evolving field. Machine learning is a part of Artificial Intelligence that consists of machines being trained on huge data samples. They can independently analyze data sets and detect patterns, trends, relationships, and such (for example the speed and pattern of COVID-19 spread). Scientists can now type a simple question into a search engine tied to the database, such as “Is COVID-19 seasonal?”, and the search engine quickly finds and ranks all related papers for them.

What’s even more important than pure knowledge? Detecting the threat. 

Another important piece of information on COVID-19 emerges daily, somewhere in the world. Collecting them fast and efficiently could probably help us prevent the next pandemic. The challenge is to fish this crucial information from other, irrelevant observations. And this exact objective is at the core of epidemic intelligence. The aim is to spot and assess epidemics, ideally in their earliest stage. Health ministries and centers for disease control in all member states of the United Nations, as well as medical and healthcare professionals, will use these newest tools for ongoing epidemic research.