Optimizing clinical research with AI

Let’s take an example: “You should know that approximately 85% of clinical trials aimed at developing new treatments end in failure before being approved by the European or American drug agencies”, indicates Douglas Teodoro, assistant professor at the Department of Radiology and Medical Informatics at the University of Geneva (UNIGE). In question, among other things, the presence of flaws in the research protocols, written by the various researchers involved and which include all the information on the method, the objectives, the progress, etc., of the study. “We have registers in which no less than 500,000 clinical trials, whether successful or not, are listed. Using algorithms, we can quickly sift through and analyze these databases to assess the risk of failure and identify which parts of a new protocol are problematic.” Improving these processes could bring new drugs to market faster and thereby reduce their price, which depends in part on the costs of research – around 1.3 billion francs for each drug approved.

Prediction tool

Regarding the effectiveness of drugs, we know that it can vary depending on the individual. The creation of homogeneous cohorts, using AI, could be useful for predicting the response to treatments of people sharing the same characteristics for the purpose of personalized medicine, explains Douglas Teodoro: “We use algorithms to give a better digital representation of each person, their health characteristics, disease, genetics, background, etc. The variables are so numerous that even the best doctor cannot assimilate them alone.”


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Optimizing clinical research with AI

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