The world we live in today is one where individualized and personalized experiences have become the norm. From the music we listen to to the TV shows we stream to the purchases we make, these are often recommendations based on data collected about us, including our streaming and purchase histories. We often take this ability to know and understand our wants and needs for granted. When it comes to managing our health and the way we take care of ourselves, the situation is much the same. Wearable devices like smartwatches and fitness trackers are becoming increasingly worn out and have made it possible to monitor our 'health stats' like heart rate, calories burned, and hours of sleep. All of this is vital data that we need to be more effective in using information about how we eat, sleep, and exercise. In addition to how we monitor our own health, the pharmaceutical industry is also examining this data to take an increasingly personalized approach to designing therapies and treatments, to accurately predict and manage any health problems that may arise. in certain groups of patients. Despite the advances of the pharmaceutical industry in the development of personalized treatments, there is still work to be done before health care adapts to each of our needs. To achieve this, we need vast amounts of data and information about different people to create truly personalized medicine and care, and often these huge data sets cannot be manually collected and analysed. If you combine this challenge with the complexity of the human body, it means that we still have a very poor understanding of how the mechanisms of the human body react to and deal with different diseases. This is where sophisticated technologies like machine learning, to help manage the amount of data being disseminated, are crucial. Fortunately, we are in a position where this technology is available to us. We just need to apply it the right way to get the most out of its use and the information it can provide with electronic medical records, to potentially save lives and revolutionize healthcare as we know it.
The data boom for personalized healthcare
While we're not there yet, truly personalized medicine at scale is only a few years away, and AI technology will be a key factor in making it happen. The amount of data we collect is growing dramatically, and IDC research predicts that the global datasphere will grow from 33 zettabytes of data in 2018 to 175 zettabytes by 2025. To put that in perspective, load 175 zettabytes of data into The Speed. average Internet connection would take 1.800 billion years. This huge data set, which includes genetic information and electronic health records like medical history and allergies, has allowed doctors to take a closer look at individual patients and their conditions in ways they don't. I couldn't have done it before. Now they can take advantage of machine learning to identify trends, patterns, and anomalies in data that can help experts make more informed decisions. The application of data analytics is also important in personalizing clinical trials and the experiences of those who enroll in them. Many trials are still being done giving the same drug or treatment to many different people and using a majority response statistical approach. This is not a "personalized" approach, as each human being has a unique genetic makeup and specific biomarkers. As a result, the effectiveness of drugs may differ from person to person, and this should be reflected in the way clinical trials are conducted.
Create a clear view of each patient
Each of us has a unique variation of the human genome, so the ability to understand which mutations or genetic differences can cause specific diseases will allow doctors to predict a health problem before it occurs and prevent it from happening. develop. This understanding lends itself to more comprehensive disease management plans to mitigate risks when they arise. Cancer treatments are an example of early intervention in action. A few years ago, the same treatment was used routinely in patients with the same type and stage of cancer. However, we now understand that different people may experience unique genetic changes in their cancer cells and/or that your genetics will affect how your body responds to cancer; these two factors will affect the progression of your cancer. With a better understanding of disease progression through analysis of patient data, precision medicine and targeted therapies can be developed and used to help predict which treatments a patient's tumor is most susceptible to. reply. In order to provide personalized medicine to this extent, it is essential to have a complete vision of each patient. To do this, we need to bring data together on a daily basis with health records and lifestyle behaviors from disparate sources into one comprehensive view. This data is crucial to understanding and analyzing the needs of each patient, which can be used to inform both how medicines are developed and the type of care a patient receives. It is these huge data sets that hold vital clues about how chronic diseases manifest so that pharmacists and clinicians can identify trends between lifestyles and developing diseases to provide earlier intervention. However, the ability to do so depends on the ability to collect, map, and analyze insights from vast amounts of data from disparate sources, a process that cannot be done manually. To put into perspective the amount of energy required to manually process the data, it would take the equivalent of the power output of the sun for an entire week just to model the genome of a single human. This is clearly not a sustainable model and will not allow us to personalize healthcare on a large scale.
AI: the key ingredient in truly personalized medicine
This is where AI comes into play and can offer tremendous benefits in solving the main challenges healthcare providers face when it comes to big data: speed, volume, variety, and veracity. In fact, nearly 80% of respondents to a recent Oracle Health Sciences survey revealed that they expect artificial intelligence and machine learning to improve treatment recommendations for people. The advantages are obvious. Thanks to artificial intelligence and machine learning capabilities, pharmaceutical companies can collect, store and analyze large data sets at a much faster rate than through manual processes. This allows them to conduct research more quickly, based on genetic variation data from large numbers of patients, and to develop targeted therapies more quickly. Furthermore, it provides a clearer view of how specific small groups of patients with certain common characteristics respond to treatments, and therefore how to accurately map the correct amounts and doses of treatments to be given to individuals. As a result, this optimizes the level of patient care that doctors can provide. In an ideal world, we want to prevent disease. By having more information at our fingertips about why, how and in whom diseases develop, we can introduce preventative measures and treatments much earlier, sometimes even before the patient begins to show symptoms.
How can personalized medicine progress?
Personalized medicine has the potential to improve and even save the lives of many people, and artificial intelligence and machine learning are the drivers of future advances. By harnessing its power with cloud computing processing, we can also begin to reap the benefits of the most innovative technologies emerging in the industry, including the use of 3D printing to deliver a personalized dose of a drug every time. patient. As wearable technologies and IoT devices continue to increase, with 1.300 billion IoT subscriptions expected by 2023 and 26.600 billion IoT devices in use by 2019, the amount of personal data we collect on ourselves will only increase, opening up more opportunities for personalized healthcare experiences for patients. There are still many challenges ahead for personalized medicine and a way forward to perfect it. But as AI becomes more widely adopted in medicine, a viable, efficient and personalized healthcare future can certainly be realized.