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Can AI/ML help solve underrepresentation in clinical trials?

By Tim Sandle, Clinical Leader

Artificial intelligence (AI) is becoming more commonplace in clinical trials, helping to advance drug development. Forms of AI include natural language processing, graph vectorization, and supervised/unsupervised learning techniques on clinical data to improve and accelerate critical steps in the clinical trials process. For example, AI is used to scan through millions of data points, drawn from published research papers, to predict the absorption, distribution, metabolism, excretion, and toxicity of new drug candidates and hence to find new compounds that can be used in new formulations of medicines. AI additionally carries the potential to speed up drug discovery, from what is traditionally several years to several days.

Machine learning (ML) enables powerful inferences to be made from clinical trial data sets and, through learning, the extent of this power increases over time. The more data that is input and the more often errors are corrected, the more reliable the algorithm for future performance. Greater predictive accuracy arises from inputting with heterogeneous data (including suitably representing biological sex). Hence, failing to use representative data or beginning with in-built biases in AI programming becomes a limitation on the seemingly expanding capacity of AI to deliver improved clinical trial outcomes. Read more …