AI In Clinical Trials – The term ‘Big Data’ is very familiar and common in all types of conversations and writing associated with the concept of a large volume of data from different sources. There’s a lot of information that it would be impossible to utilize with a conventional method. It’s here that Artificial Intelligence (AI) can be applied with two of its essential components: machine learning and deep learning.
What is machine learning?
Machine learning is what computers do. Through algorithms (necessary steps to solve a problem), computers can sort the data, generate patterns and learn without being programmed for it, and thus make decisions automatically.
However, the volume of data increases exponentially and thus requires increasingly complex algorithms. This makes the other component of machine learning important: deep learning. This is deeper learning that copies the human brain functions with neural networks in several layers, going from the simplest to the most complex learning, perfecting itself from one level to the next. Deep learning has been applied a lot and it has the ability to do something fast not only in the field of medicine but also in sports, entertainment, etc.
Machine learning has helped companies establish the case of leukemia in which cell population is very important to both biologically understand the disease and determine the targets in which to direct the treatment and improve the results for the patients.
For example, when a patient is suspected of suffering kidney failure. An ultrasound is usually done to see its appearance and if necessary, they’re referred to a diuretic x-ray which is more invasive. Radiologists who have many years of experience are able to know the degree of severity just by looking at an ultrasound test. What we can do is to train or program the machine on the logical process that the professional radiologist uses so that they can analyze the image accurately.
The importance of Clinical Trials
Clinical trials are the main methods where medical researchers study whether a new treatment, medication or device is effective and safe for people. Recruitment and retention of patients is a complicated task. According to Publicis Health, 50% of sites recruit one or no patients; 85% of clinical trials fail to retain the necessary patients; and 80% of all clinical trials fail to complete within the planned time frame. Each day that a clinical trial is delayed can cause losses of 600,000 to 8 million dollars by the sponsors of the pharmaceutical sector. AI offers massive opportunities for patient recruitment in clinical trials.
What contribution can Artificial Intelligence make to personalized medicine today?
Artificial Intelligence is positioned as a fundamental tool to reveal complexities of human biology, develop new therapeutic alternatives to treat and even cure diseases, access molecular diagnoses and collaborate in making clinical decisions based on the huge wealth of information.
The integration of artificial intelligence in medical research will open new ways of making decisions about the prognosis of patients and their treatment. This is about integrating it into their lives and make it better.
Artificial intelligence can help and assist in different areas of medicine, especially in clinical trials. Essentially, it’s teaching a machine how to do something by showing different examples. The only problem is that they’re not normally shared in the field of medicine for privacy reasons. This is why there are initiatives made by different organizations that create a database of free-to-access medical images, reports, and lifestyles of volunteers which is really helpful in the application of AI.
