Uploaded July 2025 | Updated September 2026, 2 weeks ago
We sat down with Jing Dai, Director of Biostatistics at Jazz Pharmaceuticals, to explore this question. She shared how her team is leveraging machine learning to tackle tough challenges like high placebo response and patient attrition, areas where traditional statistical models often fall short.
Jing also explains what it takes to build AI/ML models that are not only effective but also compliant with key regulatory frameworks, ensuring they are ready for regulatory review. Plus, she highlights why fostering real interdisciplinary collaboration between biostatisticians, clinicians, and data scientists is key to success.
We sat down with Jing Dai, Director of Biostatistics at Jazz Pharmaceuticals, to explore this question. She shared how her team is leveraging machine learning to tackle tough challenges like high placebo response and patient attrition, areas where traditional statistical models often fall short.
Jing also explains what it takes to build AI/ML models that are not only effective but also compliant with key regulatory frameworks, ensuring they are ready for regulatory review. Plus, she highlights why fostering real interdisciplinary collaboration between biostatisticians, clinicians, and data scientists is key to success.
