Description

In this QCast episode, Jullia and Tom unpack how machine learning is being applied across the pharmaceutical industry. They discuss what machine learning means in a regulated drug development context, where it is already supporting discovery, development, and trial operations, and how teams can use these methods responsibly without undermining scientific or regulatory confidence.

Key Takeaways

• Understand how machine learning differs from traditional statistical approaches, and why it is particularly useful for prediction and pattern recognition in complex, high dimensional data.
• Recognise where machine learning is delivering tangible benefits across discovery, clinical trials, and real world evidence, while remaining subject to the same data integrity and governance expectations as other methods.
• Apply practical best practices, including starting with clear questions, investing in data quality and validation, and balancing model performance with transparency to support trust and regulatory acceptance.

🔗 Learn More & Get Support: Visit quanticate.com to explore our biometrics services and discover how we can support your next clinical trial.
📝 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.