RadTimeline: Timeline Summarization for Longitudinal Radiological Lung Findings

TL;DR

RadTimeline introduces a structured timeline summarization method for longitudinal radiology reports, enabling better tracking of lung findings over time with a new dataset and analysis of LLM-based approaches.

Contribution

This work presents a novel structured summarization task for radiology reports, a new dataset RadTimeline, and insights into LLM prompting strategies for timeline generation.

Findings

Group name generation improves grouping accuracy

Best models achieve human-level grouping performance

Tradeoffs observed between model size and summarization quality

Abstract

Tracking findings in longitudinal radiology reports is crucial for accurately identifying disease progression, and the time-consuming process would benefit from automatic summarization. This work introduces a structured summarization task, where we frame longitudinal report summarization as a timeline generation task, with dated findings organized in columns and temporally related findings grouped in rows. This structured summarization format enables straightforward comparison of findings across time and facilitates fact-checking against the associated reports. The timeline is generated using a 3-step LLM process of extracting findings, generating group names, and using the names to group the findings. To evaluate such systems, we create RadTimeline, a timeline dataset focused on tracking lung-related radiologic findings in chest-related imaging reports. Experiments on RadTimeline show tradeoffs of different-sized LLMs and prompting strategies. Our results highlight that group name generation as an intermediate step is critical for effective finding grouping. The best configuration has some irrelevant findings but very good recall, and grouping performance is comparable to human annotators.