POET: Protocol Optimization via Eligibility Tuning
🤖 AI Summary
This work addresses the time-consuming and cognitively demanding process of drafting clinical trial eligibility criteria, which existing automated approaches often fail to support effectively due to reliance on structured inputs or limited controllability. The authors propose a guided generation framework grounded in semantic axes—such as demographics, laboratory values, and behavioral factors—that enables clinicians to steer large language models toward generating appropriate eligibility criteria without specifying exact entities. By incorporating an intermediate control mechanism and a reusable, multidimensional scoring system, the method strikes a balance between controllability and clinical utility. Experimental results demonstrate that the approach significantly outperforms unguided baselines across automatic metrics, standardized scoring rubrics, and clinician evaluations, thereby enhancing both the interpretability and practical value of AI-assisted clinical trial design.
Application Category
📝 Abstract
Eligibility criteria (EC) are essential for clinical trial design, yet drafting them remains a time-intensive and cognitively demanding task for clinicians. Existing automated approaches often fall at two extremes either requiring highly structured inputs, such as predefined entities to generate specific criteria, or relying on end-to-end systems that produce full eligibility criteria from minimal input such as trial descriptions limiting their practical utility. In this work, we propose a guided generation framework that introduces interpretable semantic axes, such as Demographics, Laboratory Parameters, and Behavioral Factors, to steer EC generation. These axes, derived using large language models, offer a middle ground between specificity and usability, enabling clinicians to guide generation without specifying exact entities. In addition, we present a reusable rubric-based evaluation framework that assesses generated criteria along clinically meaningful dimensions. Our results show that our guided generation approach consistently outperforms unguided generation in both automatic, rubric-based and clinician evaluations, offering a practical and interpretable solution for AI-assisted trial design.
Problem
Research questions and friction points this paper is trying to address.
Eligibility Criteria
Clinical Trial Design
Protocol Optimization
Guided Generation
AI-assisted Trial Design
Innovation
Methods, ideas, or system contributions that make the work stand out.
guided generation
eligibility criteria
semantic axes
rubric-based evaluation
clinical trial design
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