Thank you for Subscribing to Life Science Review Weekly Brief
Machine learning can help automate some operations that are currently primarily done manually in clinical trials, which is an excellent example of what it may contribute.
FREMONT, CA: Humans live in a quickly changing digital era that is influenced by a steady stream of ground-breaking technology advancements. Clinical research is no exception, as technology has increasingly been used in recent years to enable trials to be managed more efficiently, correctly, and creatively. This has been made possible by several technological developments, but two in particular–Artificial Intelligence (AI) and Machine Learning (ML)–are becoming increasingly important in driving industry-wide transformation.
From a Practical Perspective, What Does ML Look Like?
ML refers to algorithms that can extract significant patterns and correlations from data so that they can be used to analyze new and previously unseen data. This extraction procedure is termed the learning or the training phase. To train such an algorithm, one must provide it with data that is particular to the task. For example, one could create an algorithm that recognizes cat photographs. They would then input the algorithm model images that do and do not contain cats and provide many pictures with a variety of cats, backgrounds, and so on. This would allow the computer to extract the necessary traits to identify a cat (such as cat whiskers, paws, legs, and tails).
Machine learning techniques span from linear regression to transformer architecture, Generative Adversarial Networks (GAN), and many others. One must choose the best appropriate method for each individual use case. For example, convolutional neural networks are a form of neural network frequently utilized in the instances as mentioned above.
What Impact Will ML Have on Clinical Trials in the Future?
Making predictions in a complex field like this is always difficult. Given recent substantial breakthroughs in the field of healthcare, particularly the use of natural language processing in the mining of free text healthcare data, machine learning is almost guaranteed to have ever-increasing applicability in clinical trials.
ML can help automate some operations that are currently primarily done manually in clinical trials, which is an excellent example of what it may contribute. The cost of these tasks is exceptionally high, given the amount of data to be handled. There are several major obstacles to solve to fully realize the potential of ML and implement it in the real world in the next years, including model robustness in production, privacy protection, model interpretability, ethics difficulties, and so on. These issues apply to all machine learning applications, but they are particularly acute in healthcare and clinical trials.