Diagnosing Lower Extremity Arteriovenous Diseases Using Agentic LLMs

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Health & Wellbeing — Clinical Care & Medical Practice, Medical Specialties & Subspecialties, Medical Devices & Health Technology · Depth: Expert, short

Summary

LEA-Dialog is a new multi-turn diagnostic dialogue dataset designed for lower-extremity arteriovenous diseases, introduced by Zicen Liao, Yunhao Sun, and Matthew Purver at BioNLP 2026. This dataset is accompanied by a detailed diagnostic handbook and a process-aligned agentic framework for structured outpatient diagnosis. LEA-Dialog features stage annotations for each dialogue turn and incorporates guideline-grounded probability trends, allowing for evaluation metrics beyond just final diagnostic accuracy. Experiments demonstrate that this agentic framework significantly enhances reasoning stability and mitigates drift in both online and offline Large Language Models. Notably, smaller offline models show particularly substantial performance improvements when utilizing this framework.

Key takeaway

For NLP Engineers developing medical diagnostic tools, this research highlights the critical role of structured agentic frameworks. You should consider implementing process-aligned agentic approaches to improve reasoning stability and reduce diagnostic drift in LLM applications, especially when deploying smaller, offline models. Integrating multi-turn datasets with granular stage annotations and probability trends will also enable more robust evaluation beyond simple final accuracy metrics.

Key insights

An agentic LLM framework and specialized dataset enhance diagnostic stability for lower-extremity arteriovenous diseases.

Principles

• Structured agentic frameworks improve LLM reasoning.
• Multi-turn datasets enable granular diagnostic evaluation.
• Smaller offline LLMs benefit significantly from structured approaches.

Method

The proposed method involves a process-aligned agentic framework for structured outpatient diagnosis, leveraging a multi-turn diagnostic dialogue dataset (LEA-Dialog) and a diagnostic handbook.

In practice

• Develop multi-turn diagnostic datasets with stage annotations.
• Integrate guideline-grounded probability trends for evaluation.
• Apply agentic frameworks to stabilize LLM medical diagnostics.

Topics

• Agentic LLMs
• Medical Diagnostics
• Lower Extremity Arteriovenous Diseases
• Dialogue Datasets
• Reasoning Stability
• BioNLP

Best for: AI Scientist, Research Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.