436 – Predicting clinical trial success. Saurabh Jain & Damon Rasheed – Trial Key
In this episode we dive into the intersection of technology and clinical trials.
Join our host, Nathan Moore, as he speaks with Damon Rasheed and Saurabh Jain from TrialKey.
This episode explores the significant role predictive software plays in optimising the success rates of clinical trials in the pharmaceutical industry.
Key Takeaways
- Clinical trials are the cornerstone of discovering new treatments, and their design is pivotal to the pharmaceutical industry’s success. There is a dire need for effective trial design and the current low success rates.
- The transition from a tradition-bound trial design process to a data-driven, predictive model approach using machine learning and natural language processing (NLP) is a dramatic leap forward.
- Key technical insights are shared on how NLP and other AI tools are used to sift through vast amounts of data to recognise patterns and predict outcomes of trials, even before they begin.
- Real-world applications of predictive software in trial outcomes are in early stages, but the potential changes to the investment strategy in the pharmaceutical industry are a topic of considerable interest.
- There is hope for future developments in clinical trial design and the enhanced role of predictive software, in an industry on the cusp of breakthrough efficiency and effectiveness thanks to technology.
Check out the episode and full show notes on the Talking HealthTech website.
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