Episode Details

AI and machine learning can help research teams make more informed clinical trial design decisions, from assessing protocol feasibility to identifying suitable patient populations. In this QCast episode, Jullia and Tom discuss how predictive modelling, natural language processing, and retrospective analysis can support study planning.

Key Takeaways

• AI can help identify restrictive eligibility criteria, assess patient burden, and inform protocol feasibility before enrolment.
• Predictive modelling and retrospective analysis can support patient selection and identify treatment response patterns worth investigating in future trials.
• AI tools require appropriate validation, representative data, and human oversight, with their value assessed against measurable study outcomes.

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📝 Episode Notes & Transcript: Find show notes, resources, and a full transcript at quanticate.com/podcast.
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About Quanticate

Founded in 1994, Quanticate is a biometrics-focused CRO with over 30 years' experience delivering expert clinical and post-marketing data services worldwide. Specialising in the collection, management, standardisation, analysis, and reporting of data, we support pharma and biotech companies globally with high-quality solutions for clinical development. We specialise in:

• Clinical Data Capture & Management
• Biostatistics & Statistical Consultancy
• Statistical Programming & PK/PD Analysis
• Medical Writing & Pharmacovigilance
• Regulatory & Submission Support
• AI & Automation
• Post-Marketing Safety & Real-World Data

Quanticate is committed to delivering expertise and solutions that drive the success of clinical development worldwide.