Clinical research datasets play a crucial role in the development of artificial intelligence (AI) and machine learning (ML) systems that can directly affect people’s lives.

What is FAIR and AI-Ready for datasets?

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    <h2>Presenter:</h2>
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      <li><a href="https://aireadi.org/team#Bhavesh-Patel" target="_blank" rel="noopener nofollow">Bhavesh Patel</a>, PhD. Research Professor, California Medical Innovations Institute</li>
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    <h2>Overview</h2>
    <p>Clinical research datasets play a crucial role in the development of artificial intelligence (AI) and machine learning (ML) systems that can directly affect people’s lives. Therefore, making datasets ready for AI/ML is critical to not only increase the pace of discoveries but also to address potential downstream issues such as model biases. In this talk, we will present what it means for datasets to be AI-ready, and in particular talk about the FAIR (Findable, Accessible, Interoperable, Reusable) Principles that provide a general framework for making data technically ready for machine reuse. We will provide a concrete example of making a dataset AI-ready through the dataset generated by the AI-READI project, one of the Data Generation Projects of the NIH Bridge2AI Program.</p>
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