.webp)
Although data standards (e.g., CDISC) are mandatory for clinical trial submission to FDA, the data is often un-FAIR, limiting future reuse. The Pistoia Alliance FAIR4Clin guide explores the state of FAIR in the clinical space. In this session, Giovanni Nisato from the FAIR implementation Project emphasizes how this guide adds value to clinical data, accelerating research for data practitioners like clinical data managers or analysts. His session includes topics like metadata concepts and standards, clinical trial registries, clinical data quality, and governance.
Although data standards (e.g., CDISC) are mandatory for clinical trial submission to FDA, the data is often un-FAIR, limiting future reuse. The Pistoia Alliance FAIR4Clin guide explores the state of FAIR in the clinical space. In this session, Giovanni Nisato from the FAIR implementation Project emphasizes how this guide adds value to clinical data, accelerating research for data practitioners like clinical data managers or analysts. His session includes topics like metadata concepts and standards, clinical trial registries, clinical data quality, and governance.
Scaling clinico-genomic data integration: Large pharmaceutical organizations working with external data providers used Polly to build interoperable clinico-genomic data products 6x faster.
Although purchased datasets are often labeled as "clean," they still lack interoperability—Polly's pipelines bridge this gap with robust integration and harmonization.
Information Retrieval: Drug safety monitoring teams used Polly's Knowledge Graph powered co-scientist to conversationally retrieve the right cohorts & assess drug response—cutting discovery time by 70%.
%201.svg){kind=icon}
If you’re working with complex biological data, you may be asking:
Can generative AI truly assist in scientific reasoning, not just data analysis?
What does it mean for hypothesis generation, literature review, or even designing experiments?
Could this accelerate—not replace—my discovery pipeline?
Whether you're skeptical, curious, or already experimenting with AI in your lab—this is a session designed to ground your understanding in evidence, not speculation.
.svg)
.svg)
%201%20(1).svg)
.svg)
.png){kind=spacer}
.png){kind=spacer}
.png){kind=spacer}
.png){kind=spacer}
.png){kind=spacer}
.png){kind=spacer}
.webp)
.webp)
.webp){kind=spacer}
.webp){kind=spacer}
.webp)
.webp)
.webp){kind=spacer}
.webp)
.webp)
.webp)
.webp)
.webp)
.svg)
%20(1).svg)
