Real-World Evaluation of an AI Agent Drafting Translational Impact Summaries
Introduction. Clinical and Translational Science Award (CTSA) programs must document their scholars' research impact, but assembling each scholar's record by hand takes staff an estimated 15 hours and does not scale to a full cohort. An artificial intelligence (AI) agent could serve as a tool to gather scholar data across platforms and disciplines. Methods. We built a human-in-the-loop AI agent that assembles a dossier of sourced evidence for each scholar and drafts one-sentence Translational Science Benefits Model (TSBM) impact summaries for staff review. We evaluated it in the impact-reporting workflow of one CTSA hub across 10 career-development (KL2/K12) scholars. Two evaluation staff...
| Authors | Mohammad Arvan, Amber E. Osterholt, Bailee Rue et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 July 2026 |
| Last updated at source | 21 July 2026 |
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