When it comes to clinical trials and all the work that entails, compiling data can quickly become a tedious challenge. To deconstruct the data and turn it into meaningful information, you must be able to use Machine Learning libraries to analyze and expose potential key insights. Researchers in clinical trials…

Synthetic Data in Clinical Trials and the Healthcare Industry Artificial Intelligence (AI) and Machine Learning (ML) can create synthetic data. Synthetic data has grown extremely popular in various industries because it cuts down on the time required to gather and prepare the data. It also can help preserve anonymity. …

Synthetic data is key for accelerating deep learning. Clinical Trial applications have a wide data distribution, and datasets from the real world may not fully encompass. Some of the underlying probabilities for risk evaluation during the new drug development process may not be available by using only real-world datasets. Types…

Not everyone is perfect, which is why it is crucial to understand the limitations of synthetic data in clinical trials. It will give you a better idea of what you can do with it and if you can use it in your research. Here are the top limitations of using…

Speed is what we are after in the 21st century to help us solve problems. That is why we are always looking for better solutions that can offer us quick help. Of course, artificial intelligence and machine learning have been at the forefront of this, as they offer fast solutions. …

Artificial intelligence (AI) and machine learning (ML) are the talks of the 21st-century technological landscape. The applications of these tools are spreading across various industries such as automotive, healthcare, and much more. However, the drug development industry can benefit significantly from AI and ML in clinical trials. Here are a…