The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
Nowadays, it’s difficult to pick up a mainstream newspaper or read an industry publication without seeing reference to Artificial Intelligence or AI and progress towards innovations like autonomous vehicles, or customer behavior prediction. For the biopharma industries specifically, AI represents an opportunity to avert the R&D productivity crisis with paradigm-shifting applications such as in-silico drug design, prediction of trial risks and big data analytics.
However, with every opportunity, there are risks and challenges, and in this blog, I will discuss how pharma needs to address the opacity of AI to ensure trust and credibility with all stakeholders.
News Medical interviewed Dr. Rajat Mukherjee, Statistician, and Director of Data Science at Cytel to investigate the potential of data science in clinical development.
By Munshi Imran Hossain, Software Affiliate at Cytel
Biomedical signals are electrical signals collected from the body. Some of the most common ones are the electrocardiogram (ECG) and the electroencephalogram (EEG). These signals are of great value because they can be used for diagnostic purposes. Importantly, most of them can be collected using non-invasive methods. These attributes, together with the tremendous recent advances in electronic and digital processing technology, have made biomedical signal data an important source of data used in medical diagnostics.