CLEAR-Med 双智能体框架用 SQL 锚定临床数据问答，准确率较 ChatGPT 基线提升 54.4 个百分点
Large Language Models for Structured Clinical Data Analysis: Dual-Agent Grounding and Validation
医生朋友做临床数据分析的可以看看这个 CLEAR-Med 框架，让模型先写 SQL 再独立校验，准确率从 12% 拉到 66%，还能弃答不乱猜。
论文提出 CLEAR-Med，一个双智能体框架，用 Invocation Agent 把问题翻译成可执行 SQL，再由独立调用的 Validation Agent 做校验、限次修复或弃答。
实验基于 21 家医院的 532 条去标识新生儿缺氧缺血性脑病记录，含约 1300 个变量。在 25 个查询的开发基准上重复 5 次共 125 次回答中，Invocation Agent 答对 83 次（66.
4%），无 SQL 锚定的 ChatGPT 基线仅答对 15 次（12.0%），配对提升 54.
4 个百分点。数值结论始终与已执行 SQL 关联，无法解决的案例可保守弃答。
Large Language Models for Structured Clinical Data Analysis: Dual-Agent Grounding and Validation
Objective: To develop and characterize CLEAR-Med, a dual-agent framework for natural-language analysis of structured clinical data that separates SQL-based invocation from independent validation. Methods: CLEAR-Med uses one agent to translate a question into executable Structured Query Language (SQL), retain the executed query and database result, and produce a draft. Deterministic checks and a separately invoked cross-provider Validation Agent then accept the draft, request one bounded repair, or abstain. We formalized the system as a bounded selective pipeline and evaluated CLEAR-Med's configuration and scalability, and the Invocation Agent's accuracy and consistency on a 25-query development benchmark, using a harmonized 21-site neonatal hypoxic-ischemic encephalopathy table containing 532 de-identified infant records and approximately 1,300 variables. Results: CLEAR-Med completed all six nominal scalability configurations, including 500x1300. Across 25 development-benchmark queries repeated five times, the Invocation Agent answered 83 of 125 responses correctly (66.4%; query-cluster bootstrap 95% CI, 48.0-83.2%), compared with 15 of 125 (12.0%; 95% CI, 3.2-22.4%) for the ungrounded ChatGPT baseline, a paired improvement of 54.4 percentage points (95% CI, 36.8-72.0%). Conclusion: CLEAR-Med provides a general architecture for traceable analysis of structured clinical data: numerical claims remain linked to executed SQL, and unresolved cases can fail closed. The reported experiments characterize CLEAR-Med's configuration and scalability and the Invocation Agent's accuracy, while the formal analysis establishes the encoded-property guarantee of the complete control flow; a prospective full-pipeline evaluation of the validation and abstention stages is the next stage of this work.
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include CLEAR-Med, ChatGPT, SQL, 双智能体.
Source attribution: curated from arXiv cs.AI (@Erfan D. Dehkalani, Seetha Shankaran, Abbot R. Laptook, C. Michael Cotten, P. Ellen Grant, Yangming Ou).