Use Case Work Groups
Description
Develop best practice guidance for developing and implementing an AI-enabled clinical trials solution that can automatically extract and structure eligibility criteria from trial protocols, interpret unstructured clinical notes and structured patient data from EHRs, and determine patient-trial compatibility.
Timeline
Q1 - Q2 2026
Output
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Testing & Evaluation (T&E) Framework - Clinical Trials (v1.0)
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Goals
Develop Responsible AI content that focuses on:
Best Practice Guidance
Methods, metrics, and benchmarks to evaluate responsible use of AI-enabled clinical trials solutions
Work Group Leads
Name | Organization |
|---|---|
Elizabeth Johnson | Montana State University |
Jiahui Ma | Montana State University |
Celena Wheeler | Oracle |
Lizzy Roper | Oracle |
Navin Maganti | Biotale |
Anand Cherian | Biotale |
Sebastien Rhodes | Triomics |
Ankit Kansagra | UT Southwestern |
Pawan Jindal | Prompt Opinion |
Michael Shaw | ZS |
Benjamin May | Columbia University |
Jennifer Shannon | PsychedAboutAI |
Shaalan Beg | Individual Contributor |
