How Machine Learning is Impacting Clinical Trials

Machine learning now has ever-increasing applications in clinical trials due to the recent significant developments in the field of healthcare.

FREMONT, CA: Today, people live in a highly dynamic digital era influenced by a steady stream of ground-breaking technology advancements. The clinical research sector is not an exception to the change, as technology has frequently been used in recent times to allow trials to be conducted more efficiently, reliably, and creatively.

This has been made possible by several technological developments, but there are two in particular – Artificial Intelligence (AI) and Machine Learning (ML). These technologies are becoming increasingly important in driving industry-wide transformation.

What Does Machine Learning Look Like from a Practical Perspective?

Machine learning relates to the algorithms that can extract significant patterns and connections from data to be used to analyze new and previously unknown data. The extraction procedure is commonly known as the learning of the training phase.

To train such an algorithm, users must provide it with data that is particular to the task. Machine learning techniques vary from linear regression to transformer architecture, GAN, and many others.

It is essential for the users to choose the ideal method for every individual use-cases. For example, convolutional neural networks, a form of the neural network, are frequently utilized to identify objects.

How is Machine Learning Shaping the Future of Clinical Trials?

Making predictions in a complex field like clinical trials is always difficult. But considering the recent substantial breakthroughs in the field of healthcare, particularly the use of natural language processing in the extraction of free text healthcare data, machine learning is almost destined to play an ever-increasing role in clinical trials.

Machine learning can help automate some operations primarily done manually, such as data management in clinical trials. The expense of these jobs is extremely high due to the amount of data that has to be processed.

There are many significant obstacles to solve to fully realize machine learning capabilities and implement it in the real world in the next few years, including model robustness in production, privacy protection, model interpretability, ethics issues, and so on. These issues apply to every machine learning application, but they are particularly acute in healthcare and clinical trials.

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