The Bayer Pharma Data Science & Artificial Intelligence (DS&AI) F2F meeting in Madrid was a great event! The motto Connect, Clarify & Commit came to life as we connected during networking sessions, breakout sessions and social events. At the poster session we learned about our progress embedding a product mindset and driving operational excellence, closely collaborating with our partners across R&D. We heard presentations, took part in discussions, unconference sessions, asked questions and shared ideas, setting foundations for the growth of DS&AI beyond 2023. The use of POAPs was a definite social success and I am sure you did not miss Jesús’ enthusiasm as he gave an overview of this new tool. By promoting its use, we drive new ways of using this sustainable and effective tool, ultimately helping to innovate its use by doctors, within clinical trials, for patients and more. There were fun elements as well, like the final night, where our very own flash-mob took to the dance floor and we danced until way past midnight! All this was made possible by the org team: Anke Ebert, Larsen Schnadhorst, Kathleen Thies, Sheila Elz, Stefanie Holt-Noreiks and Claudia Vogt. They have done an excellent job coordinating the event, with the perfect balance of presentations, speakers, entertainment, and networking. Now let us make sure we continue to further the impact we have on bringing innovative medicines to our patients. Sai Jasti, Head of DS&AI https://bayer.com/poap

Hi, I'm Lucas. Nice to meet you!! My passion is software engineering and music. Check out my band: https://www.excelsis-rockband.de/

Given the considerable amount of data that is collected throughout clinical trials, it can be asked if this data can be used to improve our understanding of the trial, regardless of outcome. The radiomics pilot seeks to provide a toolkit that is capable of analyzing the different modalities of information found in clinical trials (genome, gene expression, biomarker, clinical information, and imaging information) to derive insights into why a patient may be responding, or why a clinical trial was unsuccessful. The current efforts have focused on clinical trials in oncology, while collaborating with the RED-ONC function. Overall, the project is in its pilot state, and demonstrating a proof of concept, while documenting many challenges and learnings with the development of such a toolkit. Potential directions that could expand this toolbox include deriving the factors that best predict why a patient would stay in a clinical trial, and providing assistance with patient selection of clinical trials. Other potential directions involve suggesting why a particular trial may have been unsuccessful and providing that information back to the drug development team.