How Generative AI and Knowledge Graphs Will Transform Clinical Trial Programming
The advent of modern clinical trials to evaluate drug safety and effectiveness ushered in the age of clinical data programming. To date the pharmaceutical industry relies mainly on proprietary statistical programming languages to curate, transform and apply statistical tests to create the documents regulatory authorities require to assess a drugs safety and effectiveness for patients. We will yet depend on this process for years to come. However, the number of drugs and the clinical data they generate is increasing and the time to bring them to market to meet patient needs is decreasing.
New approaches are thus required to communicate clinical data insights to regulatory authorities with increased speed and confidence. This presentation describes the main challenges we see today and considers where the opportunities exist to overcome these. We focus on the application of machine learning technologies including semantic data modelling or 'knowledge graphs'. Additionally, with the recent availability of LLM technology, popularly known as chatGPT, we also consider if and how this will support the domains evolution to meet the demands of current and future clinical trial data processing.
Speaker: Karl Brand, Bayer
Karl is originally from Ontario, Canada. He graduated as a geneticist from Melbourne University in Australia and obtained his PhD in circadian genomics from the Erasmus Medical Center (EMC) in Rotterdam, The Netherlands. A subsequent post-doctoral fellowship within the Dept. of Bioinformatics, EMC supported by the Dutch Center for Translational Molecular Medicine identified predictive biomarkers in patients with heart disease through the commercial partnering of both clinical and basic research. At Bayer since 2016 and now in his role as a study biomarker lead, he uses his experience with open source and emerging digital technologies and frameworks to drive innovation around clinical trial data towards faster, deeper, more trustful insights on behalf of patients mana
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